{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\SS Powergrabbing Replication Files\SS_Powergrabbing_replication_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Nov 2023, 20:35:59

{com}. do "C:\Users\swhitt\Desktop\SS Powergrabbing Replication Files\SS_Powergrabbing_replicationdofile.do"
{txt}
{com}. *Replication Instructions for Public Support for Power-grabbing after Civil Conflict
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the dataset “SS_Powergrabbing_replicationdata.dta”. 
. *Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication.  
. 
. *Note: You may need to install STATA packages for the cibar and catcibar command. Use findit with the command name to identify and download the appropriate packets to install. 
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. 
. *To format a graph, simply run the command to generate the graph in the do file in STATA, then open the “Graph Editor” in STATA and click on the GREEN
. *“Play Recording” button, then select “Browse” to select the grec file from the folder “grec files for STATA graph formatting” among Replication files.
. *The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. 
. *This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *Manuscript Replication
. 
. *Replication in Text
. 
. *“The average number of respondents sampled in each neighborhood was 22.8 (SD = 10.5) across 21 neighborhoods in Mosul.”
. 
. sum locale 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}locale {c |}{res}        537    22.77095    10.52616          3         39
{txt}
{com}. 
. *“The average respondent is 30 years old (ranging from 18-60) and has completed secondary education. 
. *A quarter of the sample is unemployed, and 16% of the sample cannot meet basic living expenses. 
. *The sample is almost entirely Arab and Sunni Muslim.”
. 
. sum age 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        537    30.06331    8.418823         18         60
{txt}
{com}. sum education 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}education {c |}{res}        537    3.344507    .6482846          1          4
{txt}
{com}. tab education 

         {txt}6. {c |}
  Education {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       none {c |}{res}          4        0.74        0.74
{txt}    primary {c |}{res}         40        7.45        8.19
{txt}  secondary {c |}{res}        260       48.42       56.61
{txt}     higher {c |}{res}        233       43.39      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. tab unemployed 

      {txt}dummy {c |}
   variable {c |}
        for {c |}
unemploymen {c |}
    t (from {c |}
typeofwork) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        402       74.86       74.86
{txt}          1 {c |}{res}        135       25.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. tab income 

                               {txt}8.income {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
doesn't cover expenses, significant dif {c |}{res}         86       16.01       16.01
{txt}doesn't cover expenses, some difficulti {c |}{res}        148       27.56       43.58
{txt}  covers expenses, without difficulties {c |}{res}        208       38.73       82.31
{txt}              covers expenses, can save {c |}{res}         95       17.69      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        537      100.00
{txt}
{com}. tab ethnicity 

{txt}11.ethnicit {c |}
          y {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Arab {c |}{res}        509       94.79       94.79
{txt}       Kurd {c |}{res}         25        4.66       99.44
{txt}    Turkmen {c |}{res}          3        0.56      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. tab religion 

 {txt}9.religion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Sunni {c |}{res}        521       97.02       97.02
{txt}       Shia {c |}{res}          4        0.74       97.77
{txt}  Christian {c |}{res}         12        2.23      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. 
. *“We begin with a pre-treatment analysis of the dependent variable. All respondents were asked “Do you expect the following in Mosul to get better, 
. *stay the same, or become worse over the next 12 months?” on items related to security, crime, policing, and the economy with response options ranging
. *from 1 = become a lot worse to 5 = become a lot better.”
. 
. *Principal component factor analysis indicates that responses to each of the four security-related items line up strongly on a single dimension in 
. *Mosul (see appendix for results), suggesting that the items are all capturing a latent security variable. 
. *To simplify the analysis, we create a combined latent security index based on the interim covariance of all four items and report the results for each index component in the appendix.”
. 
. *To generate the security index DV (results are consistent using factor analysis to generate DV)
. *alpha revpresecurity revprecriminal revprepolicing revpreeconomy, gen (revprealpha)
. *alpha revpostsecurity revpostcriminal revpostpolicing revposteconomy, gen (revpostalpha)
. *factor revpresecurity revprecriminal revprepolicing revpreeconomy
. *predict(revprefactor)
. *factor revpostsecurity revpostcriminal revpostpolicing revposteconomy
. *predict(revpostfactor)
. 
. *“In general, pre-treatment expectations of future security are in the cautiously positive direction (mean=3.89)”
. 
. sum revprealpha 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}revprealpha {c |}{res}        537    3.887803    .5970272       1.75          5
{txt}
{com}. 
. *“Consistent with H1, respondents view same-sided power-grabbing as security-enhancing. 
. *The Local police power-grabbing treatment (mean=3.96) produces better security expectations than power-sharing (mean=3.20).”
. 
. tab txt2

       {txt}treatment groups {c |}
   (powersharing = base {c |}
                   cat) {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
          Power-sharing {c |}{res}        173       32.22       32.22
{txt}Local Police power-grab {c |}{res}        175       32.59       64.80
{txt}       Hashd power-grab {c |}{res}        189       35.20      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        537      100.00
{txt}
{com}. sum revpostalpha if txt2==2

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
revpostalpha {c |}{res}        175    3.957143    .5971848       1.25          5
{txt}
{com}. sum revpostalpha if txt2==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
revpostalpha {c |}{res}        173    3.200867    .9833863        1.5          5
{txt}
{com}. 
. *“In fact, security expectations in Mosul under the power-sharing treatment actually drop below the pre-treatment baseline (mean=3.89), 
. *indicating how simply informing people about the status quo security conditions, which respondents may not have been aware of, has a small negative impact.”
. 
. sum revprealpha 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}revprealpha {c |}{res}        537    3.887803    .5970272       1.75          5
{txt}
{com}. 
. *“Our results also lend support to the security-reducing effects of opposing-sided power-grabs. 
. *Respondents reject opposing-sided power grabs, as suggested by H1. They react negatively 
. *to both Hashd power-grabbing (mean=3.08) and the status quo power-sharing treatment (mean=3.20) relative to Local Police power-grabbing (mean=3.96).”
. 
. sum revpostalpha if txt2==3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
revpostalpha {c |}{res}        189    3.082011    .6578246          1          5
{txt}
{com}. sum revpostalpha if txt2==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
revpostalpha {c |}{res}        173    3.200867    .9833863        1.5          5
{txt}
{com}. sum revpostalpha if txt2==2

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
revpostalpha {c |}{res}        175    3.957143    .5971848       1.25          5
{txt}
{com}. 
. *“…the local police power-grab treatment (the in-group treatment) has a strong, positive effect on security expectations, as H1 would lead us to expect (Cohen’s d=0.93).” 
. 
. *Cohen’s d for local police power grab txt relative to power-sharing
. 
. cohend revpostalpha localpolicepgtxt if hashdpgtxt~=1

{res}(1) Cohen's {it:d} and (2) Cohen's {it:d} corrected for uneven groups

{txt}(1) -.93352934
(2) -.93354476


{res}(3) Hedges' {it:g} and (4) Hedges' {it:g} corrected for uneven groups

{txt}(3) -.93084292
(4) -.93085829


{res}(5) Effect size {it:r} and (6) Effect size {it:r} corrected for uneven groups

{txt}(5) -.42295839
(6) -.42295265

{com}. 
. *“…individual security expectations in the Hashd power-grabbing and power-sharing treatments decline following treatment (Cohen’s d=-0.97 and -1.12 respectively), consistent with H1 and the visual representations in Figure 1.”
. 
. *Cohen’s d for the change in security expectations in the local police power grab treatment relative to power-sharing and the hashd power grab treatment. 
. 
. cohend revdalpha localpolicepgtxt if hashdpgtxt~=1

{res}(1) Cohen's {it:d} and (2) Cohen's {it:d} corrected for uneven groups

{txt}(1) -.97094435
(2) -.97096038


{res}(3) Hedges' {it:g} and (4) Hedges' {it:g} corrected for uneven groups

{txt}(3) -.96815026
(4) -.96816625


{res}(5) Effect size {it:r} and (6) Effect size {it:r} corrected for uneven groups

{txt}(5) -.43672773
(6) -.43672189

{com}. cohend revdalpha localpolicepgtxt if powersharetxt~=1

{res}(1) Cohen's {it:d} and (2) Cohen's {it:d} corrected for uneven groups

{txt}(1) -1.1280128
(2) -1.128848


{res}(3) Hedges' {it:g} and (4) Hedges' {it:g} corrected for uneven groups

{txt}(3) -1.1249096
(4) -1.1257425


{res}(5) Effect size {it:r} and (6) Effect size {it:r} corrected for uneven groups

{txt}(5) -.49125763
(6) -.49098179

{com}. 
. *“Figure 2 indicates that nearly half of the sample (47%) reported punishment by ISIS during the 2014-2017 period of ISIS rule.”
. 
. tab punishedisis 

 {txt}32a a)Were {c |}
        you {c |}
punished in {c |}
any way for {c |}
  violating {c |}
ISIS rules? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        286       53.26       53.26
{txt}          1 {c |}{res}        251       46.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. 
. *“Less than 10% also reported having family members injured, killed, or imprisoned by ISIS or having their homes and property confiscated by ISIS during that time.”
. 
. sum fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fleehomeisis lootedisis 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
fampunishe~s {c |}{res}        537    .0856611    .2801239          0          1
{txt}{space 1}injuredisis {c |}{res}        537    .0018622    .0431532          0          1
{txt}faminj~disis {c |}{res}        537     .027933    .1649344          0          1
{txt}famkilledi~s {c |}{res}        537    .0446927    .2068211          0          1
{txt}impris~disis {c |}{res}        537    .0912477    .2882295          0          1
{txt}{hline 13}{c +}{hline 57}
fleehomeisis {c |}{res}        537    .0223464    .1479452          0          1
{txt}{space 2}lootedisis {c |}{res}        537    .0633147    .2437552          0          1
{txt}
{com}. 
. *“More respondents claim victimization by ISIS during the 2017 liberation in the form of personal injury (39%), being detained/imprisoned by ISIS (34%), 
. *family members being injured (39%) or killed (37%), homes damaged or destroyed (27%), property looted (42%), and 36% reported that women in their families 
. *were abused or assaulted by ISIS.”
. 
. sum injuredlibisis imprisonedlibisis faminjuredlibisis famkilledlibisis homedestroyedlibisis lootedlibisis womenabusedlibisis  

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
injuredlib~s {c |}{res}        537    .3910615    .4884431          0          1
{txt}impris~bisis {c |}{res}        537    .3407821    .4744142          0          1
{txt}faminj~bisis {c |}{res}        537    .3947858    .4892604          0          1
{txt}famkilledl~s {c |}{res}        537    .3724395    .4839053          0          1
{txt}homedestro~s {c |}{res}        537    .2700186    .4443831          0          1
{txt}{hline 13}{c +}{hline 57}
lootedlibi~s {c |}{res}        537    .4189944    .4938545          0          1
{txt}womena~bisis {c |}{res}        537    .3556797    .4791651          0          1
{txt}
{com}. 
. *“Fewer than 5% indicated victimization by the Iraqi army, although 8% reported being wounded and 16% reported homes being damaged or destroyed during coalition airstrikes.”
. 
. sum injuredlibarmy homedestroyedlibarmy imprisonedlibarmy lootedlibarmy 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
injuredlib~y {c |}{res}        537    .0018622    .0431532          0          1
{txt}homedestro~y {c |}{res}        537    .0018622    .0431532          0          1
{txt}imprisoned~y {c |}{res}        537    .0074488    .0860645          0          1
{txt}lootedliba~y {c |}{res}        537    .0018622    .0431532          0          1
{txt}
{com}. 
. sum injuredlibair faminjuredlibair famkilledlibair homedestroyedlibair fleehomelibair 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
injuredlib~r {c |}{res}        537    .0819367     .274524          0          1
{txt}faminjured~r {c |}{res}        537    .0391061    .1940282          0          1
{txt}famkilledl~r {c |}{res}        537    .0260708    .1594944          0          1
{txt}homedestro~r {c |}{res}        537    .1638734    .3705058          0          1
{txt}fleehomeli~r {c |}{res}        537    .0055866    .0746039          0          1
{txt}
{com}. 
. *“Finally, we identify 24% of our sample who experienced both victimization by ISIS (during or before liberation) as well as victimization by the Iraqi military during liberation.”
. 
. tab crossfire

  {txt}crossfire {c |}
victimizati {c |}
   on dummy {c |}
   variable {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        406       75.61       75.61
{txt}          1 {c |}{res}        131       24.39      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        537      100.00
{txt}
{com}. 
. *Manuscript Table 1
. 
. tab txt 

       {txt}treatment groups {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
       Hashd power grab {c |}{res}        189       35.20       35.20
{txt}Local police power grab {c |}{res}        175       32.59       67.78
{txt}          Power sharing {c |}{res}        173       32.22      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        537      100.00
{txt}
{com}. sum ib2.txt 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}txt {c |}
Hashd pow..  {c |}{res}        537    .3519553    .4780254          0          1
{txt}Power sha..  {c |}{res}        537    .3221601    .4677397          0          1
{txt}
{com}. sum i.txt 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}txt {c |}
Local pol..  {c |}{res}        537    .3258845    .4691414          0          1
{txt}Power sha..  {c |}{res}        537    .3221601    .4677397          0          1
{txt}
{com}. sum gender age education ib2.typeofwork ib2.income ib2.religion ib2.ethnicity 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        537    .2662942    .4424321          0          1
{txt}{space 9}age {c |}{res}        537    30.06331    8.418823         18         60
{txt}{space 3}education {c |}{res}        537    3.344507    .6482846          1          4
{txt}{space 12} {c |}
{space 2}typeofwork {c |}
employer/~e  {c |}{res}        537    .0837989    .2773443          0          1
{txt}{hline 13}{c +}{hline 57}
office wo..  {c |}{res}        537    .1359404    .3430448          0          1
{txt}manual wo..  {c |}{res}        537    .1769088    .3819473          0          1
{txt}agricultu..  {c |}{res}        537    .0018622    .0431532          0          1
{txt}armed for..  {c |}{res}        537    .0111732    .1052091          0          1
{txt}{space 1}unemployed  {c |}{res}        537    .2513966    .4342205          0          1
{txt}{hline 13}{c +}{hline 57}
{space 4}student  {c |}{res}        537    .1713222    .3771415          0          1
{txt}{space 2}pensioner  {c |}{res}        537    .0018622    .0431532          0          1
{txt}{space 6}other  {c |}{res}        537    .0037244    .0609709          0          1
{txt}{space 12} {c |}
{space 6}income {c |}
doesn't c..  {c |}{res}        537     .160149    .3670861          0          1
{txt}{hline 13}{c +}{hline 57}
covers ex..  {c |}{res}        537    .3873371    .4875959          0          1
{txt}covers ex..  {c |}{res}        537    .1769088    .3819473          0          1
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 6}Sunni  {c |}{res}        537    .9702048    .1701803          0          1
{txt}{space 2}Christian  {c |}{res}        537    .0223464    .1479452          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Arab  {c |}{res}        537    .9478585    .2225197          0          1
{txt}{space 4}Turkmen  {c |}{res}        537    .0055866    .0746039          0          1
{txt}
{com}. sum gender age education i.typeofwork i.income i.religion i.ethnicity 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        537    .2662942    .4424321          0          1
{txt}{space 9}age {c |}{res}        537    30.06331    8.418823         18         60
{txt}{space 3}education {c |}{res}        537    3.344507    .6482846          1          4
{txt}{space 12} {c |}
{space 2}typeofwork {c |}
professio..  {c |}{res}        537    .1620112    .3688046          0          1
{txt}{hline 13}{c +}{hline 57}
office wo..  {c |}{res}        537    .1359404    .3430448          0          1
{txt}manual wo..  {c |}{res}        537    .1769088    .3819473          0          1
{txt}agricultu..  {c |}{res}        537    .0018622    .0431532          0          1
{txt}armed for..  {c |}{res}        537    .0111732    .1052091          0          1
{txt}{space 1}unemployed  {c |}{res}        537    .2513966    .4342205          0          1
{txt}{hline 13}{c +}{hline 57}
{space 4}student  {c |}{res}        537    .1713222    .3771415          0          1
{txt}{space 2}pensioner  {c |}{res}        537    .0018622    .0431532          0          1
{txt}{space 6}other  {c |}{res}        537    .0037244    .0609709          0          1
{txt}{space 12} {c |}
{space 6}income {c |}
doesn't c..  {c |}{res}        537    .2756052    .4472353          0          1
{txt}{hline 13}{c +}{hline 57}
covers ex..  {c |}{res}        537    .3873371    .4875959          0          1
{txt}covers ex..  {c |}{res}        537    .1769088    .3819473          0          1
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{res}        537    .0074488    .0860645          0          1
{txt}{space 2}Christian  {c |}{res}        537    .0223464    .1479452          0          1
{txt}{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{res}        537    .0465549      .21088          0          1
{txt}{space 4}Turkmen  {c |}{res}        537    .0055866    .0746039          0          1
{txt}
{com}. 
. *Manuscript Figure 1
. 
. *To install the catcibar command enter the following:
. *net install catcibar, from("https://aarondwolf.github.io/catcibar") 
. catcibar revprealpha revpostalpha, by(txt2)
{txt}
{com}. *Note additional formatting requires the "Figure 1 formatting.grec" file with the command graph play "Figure 1 formatting.grec"
.  
. *Manuscript Table 2
. 
. ttest revprealpha = revpostalpha if txt2==3

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
revpre~a {c |}{res}{col 12}    189{col 22}  3.90873{col 34} .0491937{col 46} .6763013{col 58} 3.811688{col 70} 4.005773
{txt}revpos~a {c |}{res}{col 12}    189{col 22} 3.082011{col 34} .0478497{col 46} .6578246{col 58} 2.987619{col 70} 3.176402
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    189{col 22} .8267196{col 34} .0734636{col 46} 1.009957{col 58} .6818007{col 70} .9716384
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revprealpha{txt} - {res}revpostalpha{txt})                t = {res} 11.2535
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     188

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revprealpha = revpostalpha if txt2==2

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
revpre~a {c |}{res}{col 12}    175{col 22} 3.865714{col 34} .0433962{col 46} .5740774{col 58} 3.780064{col 70} 3.951365
{txt}revpos~a {c |}{res}{col 12}    175{col 22} 3.957143{col 34} .0451429{col 46} .5971848{col 58} 3.868045{col 70} 4.046241
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    175{col 22}-.0914286{col 34}   .04013{col 46} .5308697{col 58}-.1706328{col 70}-.0122244
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revprealpha{txt} - {res}revpostalpha{txt})                t = {res} -2.2783
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     174

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}0.0120         {txt}Pr(|T| > |t|) = {res}0.0239          {txt}Pr(T > t) = {res}0.9880
{txt}
{com}. ttest revprealpha = revpostalpha if txt2==1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
revpre~a {c |}{res}{col 12}    173{col 22} 3.887283{col 34} .0399758{col 46} .5257996{col 58} 3.808377{col 70}  3.96619
{txt}revpos~a {c |}{res}{col 12}    173{col 22} 3.200867{col 34} .0747655{col 46} .9833863{col 58} 3.053291{col 70} 3.348443
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    173{col 22} .6864162{col 34} .0765354{col 46} 1.006666{col 58} .5353467{col 70} .8374857
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revprealpha{txt} - {res}revpostalpha{txt})                t = {res}  8.9686
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     172

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *For the standard deviation of the mean difference 
. 
. sum revdalpha if txt2==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}revdalpha {c |}{res}        173   -.6864162    1.006666      -2.75       1.75
{txt}
{com}. sum revdalpha if txt2==2

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}revdalpha {c |}{res}        175    .0914286    .5308697      -1.25        1.5
{txt}
{com}. sum revdalpha if txt2==3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}revdalpha {c |}{res}        189   -.8267196    1.009957       -2.5        1.5
{txt}
{com}. 
. *Manuscript Table 3a
. 
. reg revpostalpha i.txt2 , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha i.txt2 gender age education unemployed income i.religion i.ethnicity , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(11, 525)        =  {res}    30.86
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2905
                                                {txt}Root MSE          =    {res} .72602

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .5855483{col 38}{space 2} .0888018{col 49}{space 1}    6.59{col 58}{space 3}0.000{col 66}{space 4} .4110979{col 79}{space 3} .7599988
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.1252747{col 38}{space 2} .0856975{col 49}{space 1}   -1.46{col 58}{space 3}0.144{col 66}{space 4}-.2936268{col 79}{space 3} .0430774
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .1322718{col 38}{space 2} .0746921{col 49}{space 1}    1.77{col 58}{space 3}0.077{col 66}{space 4}-.0144602{col 79}{space 3} .2790039
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0201566{col 38}{space 2} .0044031{col 49}{space 1}   -4.58{col 58}{space 3}0.000{col 66}{space 4}-.0288064{col 79}{space 3}-.0115068
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1261097{col 38}{space 2} .0492663{col 49}{space 1}    2.56{col 58}{space 3}0.011{col 66}{space 4} .0293264{col 79}{space 3}  .222893
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .2192707{col 38}{space 2} .0753756{col 49}{space 1}    2.91{col 58}{space 3}0.004{col 66}{space 4} .0711959{col 79}{space 3} .3673455
{txt}{space 18}income {c |}{col 26}{res}{space 2} .0791167{col 38}{space 2} .0384909{col 49}{space 1}    2.06{col 58}{space 3}0.040{col 66}{space 4} .0035017{col 79}{space 3} .1547318
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .3519405{col 38}{space 2}  .191128{col 49}{space 1}    1.84{col 58}{space 3}0.066{col 66}{space 4} -.023529{col 79}{space 3}   .72741
{txt}{space 14}Christian  {c |}{col 26}{res}{space 2}  .248358{col 38}{space 2} .1260321{col 49}{space 1}    1.97{col 58}{space 3}0.049{col 66}{space 4} .0007688{col 79}{space 3} .4959472
{txt}{space 24} {c |}
{space 15}ethnicity {c |}
{space 19}Kurd  {c |}{col 26}{res}{space 2} .4179284{col 38}{space 2} .1100442{col 49}{space 1}    3.80{col 58}{space 3}0.000{col 66}{space 4} .2017473{col 79}{space 3} .6341094
{txt}{space 16}Turkmen  {c |}{col 26}{res}{space 2} .2179062{col 38}{space 2} .4029907{col 49}{space 1}    0.54{col 58}{space 3}0.589{col 66}{space 4}-.5737661{col 79}{space 3} 1.009578
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.11957{col 38}{space 2} .1907893{col 49}{space 1}   16.35{col 58}{space 3}0.000{col 66}{space 4} 2.744765{col 79}{space 3} 3.494374
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Manuscript Table 3b
. 
. reg revdalpha ib2.txt2, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    82.78
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1737
                                                {txt}Root MSE          =    {res} .88167

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        revdalpha{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}txt2 {c |}
{space 3}Power-sharing  {c |}{col 19}{res}{space 2}-.7778448{col 31}{space 2} .0864102{col 42}{space 1}   -9.00{col 51}{space 3}0.000{col 59}{space 4}-.9475904{col 72}{space 3}-.6080991
{txt}Hashd power-grab  {c |}{col 19}{res}{space 2}-.9181481{col 31}{space 2}  .083718{col 42}{space 1}  -10.97{col 51}{space 3}0.000{col 59}{space 4}-1.082605{col 72}{space 3}-.7536911
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} .0914286{col 31}{space 2} .0401274{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .0126016{col 72}{space 3} .1702555
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Manuscript Figure 2
. 
. graph bar punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fleehomeisis lootedisis womenabusedisis , showyvars horizontal blabel(bar, format(%4.2f)) saving(g1, replace)
{res}{txt}(note: file g1.gph not found)
{res}{txt}(file g1.gph saved)

{com}. 
. graph bar injuredlibisis faminjuredlibisis famkilledlibisis homedestroyedlibisis imprisonedlibisis fleehomelibisis lootedlibisis womenabusedlibisis , showyvars horizontal blabel(bar, format(%4.2f)) saving(g2, replace)
{res}{txt}(note: file g2.gph not found)
{res}{txt}(file g2.gph saved)

{com}. graph bar injuredlibarmy homedestroyedlibarmy imprisonedlibarmy lootedlibarmy , showyvars horizontal blabel(bar, format(%4.2f)) saving(g3, replace)
{res}{txt}(note: file g3.gph not found)
{res}{txt}(file g3.gph saved)

{com}. graph bar injuredlibair faminjuredlibair famkilledlibair homedestroyedlibair fleehomelibair  , showyvars horizontal blabel(bar, format(%4.2f)) saving(g4, replace)
{res}{txt}(note: file g4.gph not found)
{res}{txt}(file g4.gph saved)

{com}. graph combine "g1.gph" "g2.gph" "g3.gph" "g4.gph"
{res}{txt}
{com}. *Note additional formatting requires the "Figure 2 formatting.grec" file with the command graph play "Figure 2 formatting.grec"
. 
. *Code for Generating Victimization Indices
. 
. *ISIS Victimization (2014-2017)
. 
. *gen addvictimisis =  punishedisis + fampunishedisis + injuredisis + faminjuredisis + famkilledisis + imprisonedisis + fleehomeisis + lootedisis + womenabusedisis
. 
. *ISIS Victimization (Liberation)
. 
. *gen addvictimlibisis = injuredlibisis + faminjuredlibisis + famkilledlibisis + homedestroyedlibisis + imprisonedlibisis + fleehomelibisis + lootedlibisis + womenabusedlibisis 
. 
. *Iraqi Airstrike Victimization (Liberation)
. 
. *gen addvictimair =  injuredlibair + faminjuredlibair + famkilledlibair + homedestroyedlibair + fleehomelibair
. 
. *Crossfire Victimization
. 
. *gen addvictimisisall =  punishedisis + fampunishedisis + injuredisis + faminjuredisis + famkilledisis + imprisonedisis + fleehomeisis + lootedisis + womenabusedisis + injuredlibisis + faminjuredlibisis + famkilledlibisis + homedestroyedlibisis + imprisonedlibisis + fleehomelibisis + lootedlibisis + womenabusedlibisis
. 
. *gen crossfire = 0 if addvictimisisall ==0 | addvictimair==0
. *replace crossfire = 1 if addvictimisisall>0 & addvictimair>0 & prepost~=.
. 
. 
. *Manuscript Table 4
. 
. reg revpostalpha ib3.txt , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis ib3.txt##c.addvictimlibisis , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(8, 528)         =  {res}    78.41
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5199
                                                {txt}Root MSE          =    {res} .59549

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-1.165996{col 38}{space 2} .1578622{col 49}{space 1}   -7.39{col 58}{space 3}0.000{col 66}{space 4}-1.476111{col 79}{space 3}-.8558804
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .301741{col 38}{space 2} .2785098{col 49}{space 1}    1.08{col 58}{space 3}0.279{col 66}{space 4}-.2453824{col 79}{space 3} .8488643
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.7281968{col 38}{space 2} .0744224{col 49}{space 1}   -9.78{col 58}{space 3}0.000{col 66}{space 4}-.8743971{col 79}{space 3}-.5819964
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .9858833{col 38}{space 2} .0952885{col 49}{space 1}   10.35{col 58}{space 3}0.000{col 66}{space 4} .7986922{col 79}{space 3} 1.173074
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .473976{col 38}{space 2} .1252319{col 49}{space 1}    3.78{col 58}{space 3}0.000{col 66}{space 4}  .227962{col 79}{space 3}   .71999
{txt}{space 24} {c |}
{space 8}addvictimlibisis {c |}{col 26}{res}{space 2} .0927452{col 38}{space 2} .0357446{col 49}{space 1}    2.59{col 58}{space 3}0.010{col 66}{space 4} .0225262{col 79}{space 3} .1629642
{txt}{space 24} {c |}
{space 2}txt#c.addvictimlibisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1106241{col 38}{space 2}  .039548{col 49}{space 1}    2.80{col 58}{space 3}0.005{col 66}{space 4} .0329333{col 79}{space 3}  .188315
{txt}Local police power grab  {c |}{col 26}{res}{space 2} -.092431{col 38}{space 2} .0492074{col 49}{space 1}   -1.88{col 58}{space 3}0.061{col 66}{space 4}-.1890974{col 79}{space 3} .0042353
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.717128{col 38}{space 2} .1163413{col 49}{space 1}   31.95{col 58}{space 3}0.000{col 66}{space 4}  3.48858{col 79}{space 3} 3.945677
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha  ib3.txt##c.addvictimair , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    50.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2410
                                                {txt}Root MSE          =    {res} .74665

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2373311{col 38}{space 2} .1021022{col 49}{space 1}   -2.32{col 58}{space 3}0.020{col 66}{space 4}-.4379048{col 79}{space 3}-.0367573
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5905955{col 38}{space 2} .1011189{col 49}{space 1}    5.84{col 58}{space 3}0.000{col 66}{space 4} .3919533{col 79}{space 3} .7892376
{txt}{space 24} {c |}
{space 12}addvictimair {c |}{col 26}{res}{space 2}-.6680743{col 38}{space 2} .1358774{col 49}{space 1}   -4.92{col 58}{space 3}0.000{col 66}{space 4}-.9349976{col 79}{space 3} -.401151
{txt}{space 24} {c |}
{space 6}txt#c.addvictimair {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .5555578{col 38}{space 2} .1814552{col 49}{space 1}    3.06{col 58}{space 3}0.002{col 66}{space 4} .1990996{col 79}{space 3} .9120161
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .711439{col 38}{space 2} .1404217{col 49}{space 1}    5.07{col 58}{space 3}0.000{col 66}{space 4} .4355887{col 79}{space 3} .9872892
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.34375{col 38}{space 2} .0870582{col 49}{space 1}   38.41{col 58}{space 3}0.000{col 66}{space 4} 3.172729{col 79}{space 3} 3.514771
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##crossfire , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    52.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2455
                                                {txt}Root MSE          =    {res} .74442

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}              revpostalpha{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}txt {c |}
{space 9}Hashd power grab  {c |}{col 28}{res}{space 2}-.2414072{col 40}{space 2}  .101471{col 51}{space 1}   -2.38{col 60}{space 3}0.018{col 68}{space 4}-.4407409{col 81}{space 3}-.0420734
{txt}{space 2}Local police power grab  {c |}{col 28}{res}{space 2} .5792572{col 40}{space 2} .1024709{col 51}{space 1}    5.65{col 60}{space 3}0.000{col 68}{space 4} .3779591{col 81}{space 3} .7805554
{txt}{space 26} {c |}
{space 15}1.crossfire {c |}{col 28}{res}{space 2}-.7263975{col 40}{space 2} .1295237{col 51}{space 1}   -5.61{col 60}{space 3}0.000{col 68}{space 4}-.9808393{col 81}{space 3}-.4719557
{txt}{space 26} {c |}
{space 13}txt#crossfire {c |}
{space 7}Hashd power grab#1  {c |}{col 28}{res}{space 2}  .613881{col 40}{space 2} .1767476{col 51}{space 1}    3.47{col 60}{space 3}0.001{col 68}{space 4} .2666707{col 81}{space 3} .9610914
{txt}Local police power grab#1  {c |}{col 28}{res}{space 2} .8220415{col 40}{space 2} .1610549{col 51}{space 1}    5.10{col 60}{space 3}0.000{col 68}{space 4} .5056585{col 81}{space 3} 1.138424
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.347826{col 40}{space 2}  .086317{col 51}{space 1}   38.79{col 60}{space 3}0.000{col 68}{space 4} 3.178261{col 81}{space 3} 3.517391
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Manuscript Figure 3
. 
. reg revpostalpha ib3.txt##c.addvictimisis ib3.txt##c.addvictimlibisis , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(8, 528)         =  {res}    78.41
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5199
                                                {txt}Root MSE          =    {res} .59549

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-1.165996{col 38}{space 2} .1578622{col 49}{space 1}   -7.39{col 58}{space 3}0.000{col 66}{space 4}-1.476111{col 79}{space 3}-.8558804
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .301741{col 38}{space 2} .2785098{col 49}{space 1}    1.08{col 58}{space 3}0.279{col 66}{space 4}-.2453824{col 79}{space 3} .8488643
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.7281968{col 38}{space 2} .0744224{col 49}{space 1}   -9.78{col 58}{space 3}0.000{col 66}{space 4}-.8743971{col 79}{space 3}-.5819964
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .9858833{col 38}{space 2} .0952885{col 49}{space 1}   10.35{col 58}{space 3}0.000{col 66}{space 4} .7986922{col 79}{space 3} 1.173074
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .473976{col 38}{space 2} .1252319{col 49}{space 1}    3.78{col 58}{space 3}0.000{col 66}{space 4}  .227962{col 79}{space 3}   .71999
{txt}{space 24} {c |}
{space 8}addvictimlibisis {c |}{col 26}{res}{space 2} .0927452{col 38}{space 2} .0357446{col 49}{space 1}    2.59{col 58}{space 3}0.010{col 66}{space 4} .0225262{col 79}{space 3} .1629642
{txt}{space 24} {c |}
{space 2}txt#c.addvictimlibisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1106241{col 38}{space 2}  .039548{col 49}{space 1}    2.80{col 58}{space 3}0.005{col 66}{space 4} .0329333{col 79}{space 3}  .188315
{txt}Local police power grab  {c |}{col 26}{res}{space 2} -.092431{col 38}{space 2} .0492074{col 49}{space 1}   -1.88{col 58}{space 3}0.061{col 66}{space 4}-.1890974{col 79}{space 3} .0042353
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.717128{col 38}{space 2} .1163413{col 49}{space 1}   31.95{col 58}{space 3}0.000{col 66}{space 4}  3.48858{col 79}{space 3} 3.945677
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib3.txt, at(addvictimisis=(0 (1) 6))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       537
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:addvictimi~s}{space 4}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{res}{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}     Margin{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}_at#txt {c |}
{space 7}1#Hashd power grab  {c |}{col 28}{res}{space 2} 3.180934{col 40}{space 2} .0717276{col 51}{space 1}   44.35{col 60}{space 3}0.000{col 68}{space 4} 3.040027{col 81}{space 3}  3.32184
{txt}1#Local police power grab  {c |}{col 28}{res}{space 2} 4.019842{col 40}{space 2} .1500248{col 51}{space 1}   26.79{col 60}{space 3}0.000{col 68}{space 4} 3.725123{col 81}{space 3} 4.314561
{txt}{space 10}1#Power sharing  {c |}{col 28}{res}{space 2} 4.004345{col 40}{space 2} .0813005{col 51}{space 1}   49.25{col 60}{space 3}0.000{col 68}{space 4} 3.844633{col 81}{space 3} 4.164057
{txt}{space 7}2#Hashd power grab  {c |}{col 28}{res}{space 2}  3.43862{col 40}{space 2} .0395948{col 51}{space 1}   86.85{col 60}{space 3}0.000{col 68}{space 4} 3.360838{col 81}{space 3} 3.516403
{txt}2#Local police power grab  {c |}{col 28}{res}{space 2} 3.765621{col 40}{space 2} .1381148{col 51}{space 1}   27.26{col 60}{space 3}0.000{col 68}{space 4} 3.494299{col 81}{space 3} 4.036944
{txt}{space 10}2#Power sharing  {c |}{col 28}{res}{space 2} 3.276148{col 40}{space 2} .0725987{col 51}{space 1}   45.13{col 60}{space 3}0.000{col 68}{space 4}  3.13353{col 81}{space 3} 3.418766
{txt}{space 7}3#Hashd power grab  {c |}{col 28}{res}{space 2} 3.696307{col 40}{space 2} .0712253{col 51}{space 1}   51.90{col 60}{space 3}0.000{col 68}{space 4} 3.556387{col 81}{space 3} 3.836227
{txt}3#Local police power grab  {c |}{col 28}{res}{space 2} 3.511401{col 40}{space 2} .1895591{col 51}{space 1}   18.52{col 60}{space 3}0.000{col 68}{space 4} 3.139018{col 81}{space 3} 3.883783
{txt}{space 10}3#Power sharing  {c |}{col 28}{res}{space 2} 2.547951{col 40}{space 2} .1225102{col 51}{space 1}   20.80{col 60}{space 3}0.000{col 68}{space 4} 2.307284{col 81}{space 3} 2.788619
{txt}{space 7}4#Hashd power grab  {c |}{col 28}{res}{space 2} 3.953993{col 40}{space 2} .1251429{col 51}{space 1}   31.60{col 60}{space 3}0.000{col 68}{space 4} 3.708154{col 81}{space 3} 4.199833
{txt}4#Local police power grab  {c |}{col 28}{res}{space 2}  3.25718{col 40}{space 2}   .27033{col 51}{space 1}   12.05{col 60}{space 3}0.000{col 68}{space 4} 2.726126{col 81}{space 3} 3.788234
{txt}{space 10}4#Power sharing  {c |}{col 28}{res}{space 2} 1.819755{col 40}{space 2} .1892731{col 51}{space 1}    9.61{col 60}{space 3}0.000{col 68}{space 4} 1.447934{col 81}{space 3} 2.191575
{txt}{space 7}5#Hashd power grab  {c |}{col 28}{res}{space 2}  4.21168{col 40}{space 2} .1825673{col 51}{space 1}   23.07{col 60}{space 3}0.000{col 68}{space 4} 3.853032{col 81}{space 3} 4.570327
{txt}5#Local police power grab  {c |}{col 28}{res}{space 2} 3.002959{col 40}{space 2} .3612653{col 51}{space 1}    8.31{col 60}{space 3}0.000{col 68}{space 4} 2.293265{col 81}{space 3} 3.712653
{txt}{space 10}5#Power sharing  {c |}{col 28}{res}{space 2} 1.091558{col 40}{space 2} .2602254{col 51}{space 1}    4.19{col 60}{space 3}0.000{col 68}{space 4} .5803535{col 81}{space 3} 1.602762
{txt}{space 7}6#Hashd power grab  {c |}{col 28}{res}{space 2} 4.469367{col 40}{space 2} .2410048{col 51}{space 1}   18.54{col 60}{space 3}0.000{col 68}{space 4} 3.995921{col 81}{space 3} 4.942812
{txt}6#Local police power grab  {c |}{col 28}{res}{space 2} 2.748738{col 40}{space 2} .4563284{col 51}{space 1}    6.02{col 60}{space 3}0.000{col 68}{space 4} 1.852296{col 81}{space 3}  3.64518
{txt}{space 10}6#Power sharing  {c |}{col 28}{res}{space 2} .3633611{col 40}{space 2} .3326975{col 51}{space 1}    1.09{col 60}{space 3}0.275{col 68}{space 4}-.2902122{col 81}{space 3} 1.016934
{txt}{space 7}7#Hashd power grab  {c |}{col 28}{res}{space 2} 4.727053{col 40}{space 2} .2998636{col 51}{space 1}   15.76{col 60}{space 3}0.000{col 68}{space 4} 4.137981{col 81}{space 3} 5.316125
{txt}7#Local police power grab  {c |}{col 28}{res}{space 2} 2.494518{col 40}{space 2} .5533961{col 51}{space 1}    4.51{col 60}{space 3}0.000{col 68}{space 4} 1.407389{col 81}{space 3} 3.581646
{txt}{space 10}7#Power sharing  {c |}{col 28}{res}{space 2}-.3648357{col 40}{space 2}  .405876{col 51}{space 1}   -0.90{col 60}{space 3}0.369{col 68}{space 4}-1.162166{col 81}{space 3} .4324944
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, saving(g5, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: addvictimisis txt{p_end}
{res}{txt}(note: file g5.gph not found)
{res}{txt}(file g5.gph saved)

{com}. margins ib3.txt, at(addvictimlibisis=(0 (1) 8))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       537
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:addvictiml~s}{space 4}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}

{res}{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}     Margin{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}_at#txt {c |}
{space 7}1#Hashd power grab  {c |}{col 28}{res}{space 2} 2.762273{col 40}{space 2} .0639563{col 51}{space 1}   43.19{col 60}{space 3}0.000{col 68}{space 4} 2.636633{col 81}{space 3} 2.887913
{txt}1#Local police power grab  {c |}{col 28}{res}{space 2} 3.810569{col 40}{space 2} .2279843{col 51}{space 1}   16.71{col 60}{space 3}0.000{col 68}{space 4} 3.362702{col 81}{space 3} 4.258437
{txt}{space 10}1#Power sharing  {c |}{col 28}{res}{space 2} 3.120468{col 40}{space 2} .0854627{col 51}{space 1}   36.51{col 60}{space 3}0.000{col 68}{space 4} 2.952579{col 81}{space 3} 3.288357
{txt}{space 7}2#Hashd power grab  {c |}{col 28}{res}{space 2} 2.965642{col 40}{space 2} .0521032{col 51}{space 1}   56.92{col 60}{space 3}0.000{col 68}{space 4} 2.863287{col 81}{space 3} 3.067997
{txt}2#Local police power grab  {c |}{col 28}{res}{space 2} 3.810883{col 40}{space 2} .1966467{col 51}{space 1}   19.38{col 60}{space 3}0.000{col 68}{space 4} 3.424578{col 81}{space 3} 4.197189
{txt}{space 10}2#Power sharing  {c |}{col 28}{res}{space 2} 3.213213{col 40}{space 2}  .061495{col 51}{space 1}   52.25{col 60}{space 3}0.000{col 68}{space 4} 3.092408{col 81}{space 3} 3.334018
{txt}{space 7}3#Hashd power grab  {c |}{col 28}{res}{space 2} 3.169011{col 40}{space 2} .0437244{col 51}{space 1}   72.48{col 60}{space 3}0.000{col 68}{space 4} 3.083116{col 81}{space 3} 3.254906
{txt}3#Local police power grab  {c |}{col 28}{res}{space 2} 3.811198{col 40}{space 2} .1662841{col 51}{space 1}   22.92{col 60}{space 3}0.000{col 68}{space 4} 3.484538{col 81}{space 3} 4.137857
{txt}{space 10}3#Power sharing  {c |}{col 28}{res}{space 2} 3.305959{col 40}{space 2} .0530541{col 51}{space 1}   62.31{col 60}{space 3}0.000{col 68}{space 4} 3.201735{col 81}{space 3} 3.410182
{txt}{space 7}4#Hashd power grab  {c |}{col 28}{res}{space 2} 3.372381{col 40}{space 2} .0410079{col 51}{space 1}   82.24{col 60}{space 3}0.000{col 68}{space 4} 3.291822{col 81}{space 3} 3.452939
{txt}4#Local police power grab  {c |}{col 28}{res}{space 2} 3.811512{col 40}{space 2} .1375437{col 51}{space 1}   27.71{col 60}{space 3}0.000{col 68}{space 4} 3.541312{col 81}{space 3} 4.081712
{txt}{space 10}4#Power sharing  {c |}{col 28}{res}{space 2} 3.398704{col 40}{space 2} .0663566{col 51}{space 1}   51.22{col 60}{space 3}0.000{col 68}{space 4} 3.268348{col 81}{space 3} 3.529059
{txt}{space 7}5#Hashd power grab  {c |}{col 28}{res}{space 2}  3.57575{col 40}{space 2} .0449914{col 51}{space 1}   79.48{col 60}{space 3}0.000{col 68}{space 4} 3.487366{col 81}{space 3} 3.664134
{txt}5#Local police power grab  {c |}{col 28}{res}{space 2} 3.811826{col 40}{space 2}  .111685{col 51}{space 1}   34.13{col 60}{space 3}0.000{col 68}{space 4} 3.592424{col 81}{space 3} 4.031227
{txt}{space 10}5#Power sharing  {c |}{col 28}{res}{space 2} 3.491449{col 40}{space 2}   .09245{col 51}{space 1}   37.77{col 60}{space 3}0.000{col 68}{space 4} 3.309834{col 81}{space 3} 3.673064
{txt}{space 7}6#Hashd power grab  {c |}{col 28}{res}{space 2} 3.779119{col 40}{space 2} .0542177{col 51}{space 1}   69.70{col 60}{space 3}0.000{col 68}{space 4}  3.67261{col 81}{space 3} 3.885628
{txt}6#Local police power grab  {c |}{col 28}{res}{space 2}  3.81214{col 40}{space 2} .0911933{col 51}{space 1}   41.80{col 60}{space 3}0.000{col 68}{space 4} 3.632994{col 81}{space 3} 3.991286
{txt}{space 10}6#Power sharing  {c |}{col 28}{res}{space 2} 3.584194{col 40}{space 2} .1234753{col 51}{space 1}   29.03{col 60}{space 3}0.000{col 68}{space 4} 3.341631{col 81}{space 3} 3.826757
{txt}{space 7}7#Hashd power grab  {c |}{col 28}{res}{space 2} 3.982488{col 40}{space 2} .0665404{col 51}{space 1}   59.85{col 60}{space 3}0.000{col 68}{space 4} 3.851772{col 81}{space 3} 4.113205
{txt}7#Local police power grab  {c |}{col 28}{res}{space 2} 3.812454{col 40}{space 2} .0802887{col 51}{space 1}   47.48{col 60}{space 3}0.000{col 68}{space 4}  3.65473{col 81}{space 3} 3.970179
{txt}{space 10}7#Power sharing  {c |}{col 28}{res}{space 2} 3.676939{col 40}{space 2} .1565268{col 51}{space 1}   23.49{col 60}{space 3}0.000{col 68}{space 4} 3.369447{col 81}{space 3} 3.984431
{txt}{space 7}8#Hashd power grab  {c |}{col 28}{res}{space 2} 4.185858{col 40}{space 2} .0805509{col 51}{space 1}   51.97{col 60}{space 3}0.000{col 68}{space 4} 4.027618{col 81}{space 3} 4.344097
{txt}8#Local police power grab  {c |}{col 28}{res}{space 2} 3.812768{col 40}{space 2} .0828476{col 51}{space 1}   46.02{col 60}{space 3}0.000{col 68}{space 4} 3.650017{col 81}{space 3}  3.97552
{txt}{space 10}8#Power sharing  {c |}{col 28}{res}{space 2} 3.769684{col 40}{space 2} .1905531{col 51}{space 1}   19.78{col 60}{space 3}0.000{col 68}{space 4} 3.395349{col 81}{space 3}  4.14402
{txt}{space 7}9#Hashd power grab  {c |}{col 28}{res}{space 2} 4.389227{col 40}{space 2} .0955093{col 51}{space 1}   45.96{col 60}{space 3}0.000{col 68}{space 4} 4.201602{col 81}{space 3} 4.576852
{txt}9#Local police power grab  {c |}{col 28}{res}{space 2} 3.813082{col 40}{space 2} .0978191{col 51}{space 1}   38.98{col 60}{space 3}0.000{col 68}{space 4}  3.62092{col 81}{space 3} 4.005245
{txt}{space 10}9#Power sharing  {c |}{col 28}{res}{space 2}  3.86243{col 40}{space 2} .2251126{col 51}{space 1}   17.16{col 60}{space 3}0.000{col 68}{space 4} 3.420203{col 81}{space 3} 4.304656
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, saving(g6, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: addvictimlibisis txt{p_end}
{res}{txt}(note: file g6.gph not found)
{res}{txt}(file g6.gph saved)

{com}. reg revpostalpha  ib3.txt##c.addvictimair , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    50.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2410
                                                {txt}Root MSE          =    {res} .74665

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2373311{col 38}{space 2} .1021022{col 49}{space 1}   -2.32{col 58}{space 3}0.020{col 66}{space 4}-.4379048{col 79}{space 3}-.0367573
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5905955{col 38}{space 2} .1011189{col 49}{space 1}    5.84{col 58}{space 3}0.000{col 66}{space 4} .3919533{col 79}{space 3} .7892376
{txt}{space 24} {c |}
{space 12}addvictimair {c |}{col 26}{res}{space 2}-.6680743{col 38}{space 2} .1358774{col 49}{space 1}   -4.92{col 58}{space 3}0.000{col 66}{space 4}-.9349976{col 79}{space 3} -.401151
{txt}{space 24} {c |}
{space 6}txt#c.addvictimair {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .5555578{col 38}{space 2} .1814552{col 49}{space 1}    3.06{col 58}{space 3}0.002{col 66}{space 4} .1990996{col 79}{space 3} .9120161
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .711439{col 38}{space 2} .1404217{col 49}{space 1}    5.07{col 58}{space 3}0.000{col 66}{space 4} .4355887{col 79}{space 3} .9872892
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.34375{col 38}{space 2} .0870582{col 49}{space 1}   38.41{col 58}{space 3}0.000{col 66}{space 4} 3.172729{col 79}{space 3} 3.514771
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib3.txt, at(addvictimair=(0 (1) 4))
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       537
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:addvictimair}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:addvictimair}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:addvictimair}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:addvictimair}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:addvictimair}{space 4}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{res}{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}     Margin{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}_at#txt {c |}
{space 7}1#Hashd power grab  {c |}{col 28}{res}{space 2} 3.106419{col 40}{space 2} .0533454{col 51}{space 1}   58.23{col 60}{space 3}0.000{col 68}{space 4} 3.001625{col 81}{space 3} 3.211213
{txt}1#Local police power grab  {c |}{col 28}{res}{space 2} 3.934345{col 40}{space 2} .0514384{col 51}{space 1}   76.49{col 60}{space 3}0.000{col 68}{space 4} 3.833298{col 81}{space 3} 4.035393
{txt}{space 10}1#Power sharing  {c |}{col 28}{res}{space 2}  3.34375{col 40}{space 2} .0870582{col 51}{space 1}   38.41{col 60}{space 3}0.000{col 68}{space 4} 3.172729{col 81}{space 3} 3.514771
{txt}{space 7}2#Hashd power grab  {c |}{col 28}{res}{space 2} 2.993902{col 40}{space 2}  .107785{col 51}{space 1}   27.78{col 60}{space 3}0.000{col 68}{space 4} 2.782165{col 81}{space 3}  3.20564
{txt}2#Local police power grab  {c |}{col 28}{res}{space 2}  3.97771{col 40}{space 2} .0456724{col 51}{space 1}   87.09{col 60}{space 3}0.000{col 68}{space 4} 3.887989{col 81}{space 3} 4.067431
{txt}{space 10}2#Power sharing  {c |}{col 28}{res}{space 2} 2.675676{col 40}{space 2} .1043243{col 51}{space 1}   25.65{col 60}{space 3}0.000{col 68}{space 4} 2.470737{col 81}{space 3} 2.880615
{txt}{space 7}3#Hashd power grab  {c |}{col 28}{res}{space 2} 2.881386{col 40}{space 2} .2220723{col 51}{space 1}   12.97{col 60}{space 3}0.000{col 68}{space 4} 2.445138{col 81}{space 3} 3.317634
{txt}3#Local police power grab  {c |}{col 28}{res}{space 2} 4.021075{col 40}{space 2} .0635388{col 51}{space 1}   63.29{col 60}{space 3}0.000{col 68}{space 4} 3.896256{col 81}{space 3} 4.145893
{txt}{space 10}3#Power sharing  {c |}{col 28}{res}{space 2} 2.007601{col 40}{space 2} .2260826{col 51}{space 1}    8.88{col 60}{space 3}0.000{col 68}{space 4} 1.563475{col 81}{space 3} 2.451727
{txt}{space 7}4#Hashd power grab  {c |}{col 28}{res}{space 2} 2.768869{col 40}{space 2} .3405015{col 51}{space 1}    8.13{col 60}{space 3}0.000{col 68}{space 4} 2.099974{col 81}{space 3} 3.437765
{txt}4#Local police power grab  {c |}{col 28}{res}{space 2} 4.064439{col 40}{space 2}  .092193{col 51}{space 1}   44.09{col 60}{space 3}0.000{col 68}{space 4} 3.883332{col 81}{space 3} 4.245547
{txt}{space 10}4#Power sharing  {c |}{col 28}{res}{space 2} 1.339527{col 40}{space 2} .3581459{col 51}{space 1}    3.74{col 60}{space 3}0.000{col 68}{space 4} .6359704{col 81}{space 3} 2.043084
{txt}{space 7}5#Hashd power grab  {c |}{col 28}{res}{space 2} 2.656353{col 40}{space 2} .4598838{col 51}{space 1}    5.78{col 60}{space 3}0.000{col 68}{space 4} 1.752938{col 81}{space 3} 3.559768
{txt}5#Local police power grab  {c |}{col 28}{res}{space 2} 4.107804{col 40}{space 2} .1243907{col 51}{space 1}   33.02{col 60}{space 3}0.000{col 68}{space 4} 3.863446{col 81}{space 3} 4.352162
{txt}{space 10}5#Power sharing  {c |}{col 28}{res}{space 2} .6714527{col 40}{space 2} .4922895{col 51}{space 1}    1.36{col 60}{space 3}0.173{col 68}{space 4}-.2956213{col 81}{space 3} 1.638527
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, saving(g7, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: addvictimair txt{p_end}
{res}{txt}(note: file g7.gph not found)
{res}{txt}(file g7.gph saved)

{com}. reg revpostalpha ib3.txt##crossfire , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    52.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2455
                                                {txt}Root MSE          =    {res} .74442

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}              revpostalpha{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}txt {c |}
{space 9}Hashd power grab  {c |}{col 28}{res}{space 2}-.2414072{col 40}{space 2}  .101471{col 51}{space 1}   -2.38{col 60}{space 3}0.018{col 68}{space 4}-.4407409{col 81}{space 3}-.0420734
{txt}{space 2}Local police power grab  {c |}{col 28}{res}{space 2} .5792572{col 40}{space 2} .1024709{col 51}{space 1}    5.65{col 60}{space 3}0.000{col 68}{space 4} .3779591{col 81}{space 3} .7805554
{txt}{space 26} {c |}
{space 15}1.crossfire {c |}{col 28}{res}{space 2}-.7263975{col 40}{space 2} .1295237{col 51}{space 1}   -5.61{col 60}{space 3}0.000{col 68}{space 4}-.9808393{col 81}{space 3}-.4719557
{txt}{space 26} {c |}
{space 13}txt#crossfire {c |}
{space 7}Hashd power grab#1  {c |}{col 28}{res}{space 2}  .613881{col 40}{space 2} .1767476{col 51}{space 1}    3.47{col 60}{space 3}0.001{col 68}{space 4} .2666707{col 81}{space 3} .9610914
{txt}Local police power grab#1  {c |}{col 28}{res}{space 2} .8220415{col 40}{space 2} .1610549{col 51}{space 1}    5.10{col 60}{space 3}0.000{col 68}{space 4} .5056585{col 81}{space 3} 1.138424
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.347826{col 40}{space 2}  .086317{col 51}{space 1}   38.79{col 60}{space 3}0.000{col 68}{space 4} 3.178261{col 81}{space 3} 3.517391
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib3.txt, at(crossfire=(0 1))
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       537
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:crossfire}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:crossfire}{space 7}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}     Margin{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}_at#txt {c |}
{space 7}1#Hashd power grab  {c |}{col 28}{res}{space 2} 3.106419{col 40}{space 2} .0533454{col 51}{space 1}   58.23{col 60}{space 3}0.000{col 68}{space 4} 3.001625{col 81}{space 3} 3.211213
{txt}1#Local police power grab  {c |}{col 28}{res}{space 2} 3.927083{col 40}{space 2} .0552238{col 51}{space 1}   71.11{col 60}{space 3}0.000{col 68}{space 4} 3.818599{col 81}{space 3} 4.035567
{txt}{space 10}1#Power sharing  {c |}{col 28}{res}{space 2} 3.347826{col 40}{space 2}  .086317{col 51}{space 1}   38.79{col 60}{space 3}0.000{col 68}{space 4} 3.178261{col 81}{space 3} 3.517391
{txt}{space 7}2#Hashd power grab  {c |}{col 28}{res}{space 2} 2.993902{col 40}{space 2}  .107785{col 51}{space 1}   27.78{col 60}{space 3}0.000{col 68}{space 4} 2.782165{col 81}{space 3}  3.20564
{txt}2#Local police power grab  {c |}{col 28}{res}{space 2} 4.022727{col 40}{space 2} .0781833{col 51}{space 1}   51.45{col 60}{space 3}0.000{col 68}{space 4} 3.869141{col 81}{space 3} 4.176314
{txt}{space 10}2#Power sharing  {c |}{col 28}{res}{space 2} 2.621429{col 40}{space 2}   .09657{col 51}{space 1}   27.15{col 60}{space 3}0.000{col 68}{space 4} 2.431722{col 81}{space 3} 2.811135
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, saving(g8, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: crossfire txt{p_end}
{res}{txt}(note: file g8.gph not found)
{res}{txt}(file g8.gph saved)

{com}. 
. graph combine "g5.gph" "g6.gph" "g7.gph" "g8.gph"
{res}{txt}
{com}. *Note additional formatting requires the "Figure 3 formatting.grec" file with the command graph play "Figure 3 formatting.grec"
. 
. *Appendix Replication
. 
. *Appendix Figure 1. Fear across Treatment Groups
. 
. cibar afraid , over1(txt)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 1 mosul fear bar.grec" file with the command graph play "Figure 1 mosul fear bar.grec"
. 
. *Appendix Figure 2. Emotions across Treatment Groups
. 
. graph box afraid angry happy sad worried satisfied, by(txt2)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 2 mosul emotions.grec" file with the command graph play "Figure 2 mosul emotions.grec"
. 
. *Appendix Figure 3. Mean Pre-Treatment Security Concerns by Treatment Group
. 
. ssc install cibar
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. cibar revprealpha , over1(txt)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 3 Mean Pre-txt security mosul.grec" file with the command graph play "Figure 3 Mean Pre-txt security mosul.grec"
. 
. *To replicate the table of means below the figure
. 
. sum revprealpha if txt==1 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}revprealpha {c |}{res}        189     3.90873    .6763013       2.75          5
{txt}
{com}. sum revprealpha if txt==2 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}revprealpha {c |}{res}        175    3.865714    .5740774       1.75          5
{txt}
{com}. sum revprealpha if txt==3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}revprealpha {c |}{res}        173    3.887283    .5257996       2.75          5
{txt}
{com}. 
. *Appendix Figure 4. Pre-treatment Security Assessment by Index Components
. 
. catcibar revpresecurity revprecriminal revprepolicing revpreeconomy 
{txt}
{com}. *Note additional formatting requires the "Figure 4 Pre-txt security component means2.grec" file with the command graph play "Figure 4 Pre-txt security component means2.grec"
. 
. *Appendix Figure 5. Post-treatment Security Assessment by Index Components
. 
. catcibar revpostsecurity-revposteconomy , over(txt2)
{txt}
{com}. *Note additional formatting requires the "Figure 5 Post-txt security component means2.grec" file with the command graph play "Figure 5 Post-txt security component means2.grec"
. 
. *Principal Component Factor Analysis on Security Index
. 
. factor revpresecurity revprecriminal revprepolicing revpreeconomy
{txt}(obs=537)

Factor analysis/correlation{col 50}Number of obs    = {res}       537
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.51325      1.09291            0.9862       0.9862
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.42033      0.54616            0.2739       1.2602
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.12583      0.14754           -0.0820       1.1782
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.27337            .           -0.1782       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  549.50{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:revpresecu~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5702}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3847}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5269}}}{space 1}
{space 4}{space 0}{ralign 12:revprecrim~l}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7802}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1375}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3724}}}{space 1}
{space 4}{space 0}{ralign 12:revprepoli~g}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6119}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1679}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5973}}}{space 1}
{space 4}{space 0}{ralign 12:revpreecon~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4528}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4746}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5698}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. factor revpostsecurity revpostcriminal revpostpolicing revposteconomy
{txt}(obs=537)

Factor analysis/correlation{col 50}Number of obs    = {res}       537
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.72960      2.57531            1.0005       1.0005
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.15429      0.13851            0.0565       1.0570
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.01578      0.18709            0.0058       1.0628
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.17131            .           -0.0628       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1456.96{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:revpostsec~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8719}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1862}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0582}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2017}}}{space 1}
{space 4}{space 0}{ralign 12:revpostcri~l}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8531}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1815}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0616}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2355}}}{space 1}
{space 4}{space 0}{ralign 12:revpostpol~g}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8099}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1972}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0681}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3005}}}{space 1}
{space 4}{space 0}{ralign 12:revposteco~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7652}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2186}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0629}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3627}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Robustness Checks for MS Table 3 
. 
. *MS Table 3a using OLS with clustered standard errors at the neighborhood level
. 
. reg revpostalpha i.txt2 , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 20)          =  {res}    15.91
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{ralign 90:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .2523698{col 49}{space 1}    3.00{col 58}{space 3}0.007{col 66}{space 4} .2298417{col 79}{space 3}  1.28271
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .2425702{col 49}{space 1}   -0.49{col 58}{space 3}0.629{col 66}{space 4}-.6248489{col 79}{space 3}  .387136
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .2374863{col 49}{space 1}   13.48{col 58}{space 3}0.000{col 66}{space 4} 2.705479{col 79}{space 3} 3.696255
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha i.txt2 gender age education unemployed income i.religion i.ethnicity , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(11, 20)         =  {res}    18.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2905
                                                {txt}Root MSE          =    {res} .72602

{txt}{ralign 90:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .5855483{col 38}{space 2} .2251741{col 49}{space 1}    2.60{col 58}{space 3}0.017{col 66}{space 4} .1158433{col 79}{space 3} 1.055253
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.1252747{col 38}{space 2} .2094873{col 49}{space 1}   -0.60{col 58}{space 3}0.557{col 66}{space 4}-.5622577{col 79}{space 3} .3117082
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .1322718{col 38}{space 2} .1268012{col 49}{space 1}    1.04{col 58}{space 3}0.309{col 66}{space 4}-.1322309{col 79}{space 3} .3967746
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0201566{col 38}{space 2} .0067322{col 49}{space 1}   -2.99{col 58}{space 3}0.007{col 66}{space 4}-.0341997{col 79}{space 3}-.0061135
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1261097{col 38}{space 2} .0512701{col 49}{space 1}    2.46{col 58}{space 3}0.023{col 66}{space 4} .0191621{col 79}{space 3} .2330572
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .2192707{col 38}{space 2} .0913208{col 49}{space 1}    2.40{col 58}{space 3}0.026{col 66}{space 4} .0287789{col 79}{space 3} .4097625
{txt}{space 18}income {c |}{col 26}{res}{space 2} .0791167{col 38}{space 2}   .04066{col 49}{space 1}    1.95{col 58}{space 3}0.066{col 66}{space 4}-.0056985{col 79}{space 3} .1639319
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .3519405{col 38}{space 2} .2543577{col 49}{space 1}    1.38{col 58}{space 3}0.182{col 66}{space 4}-.1786404{col 79}{space 3} .8825213
{txt}{space 14}Christian  {c |}{col 26}{res}{space 2}  .248358{col 38}{space 2} .1675756{col 49}{space 1}    1.48{col 58}{space 3}0.154{col 66}{space 4}-.1011986{col 79}{space 3} .5979147
{txt}{space 24} {c |}
{space 15}ethnicity {c |}
{space 19}Kurd  {c |}{col 26}{res}{space 2} .4179284{col 38}{space 2} .1121925{col 49}{space 1}    3.73{col 58}{space 3}0.001{col 66}{space 4}  .183899{col 79}{space 3} .6519578
{txt}{space 16}Turkmen  {c |}{col 26}{res}{space 2} .2179062{col 38}{space 2} .3550993{col 49}{space 1}    0.61{col 58}{space 3}0.546{col 66}{space 4} -.522818{col 79}{space 3} .9586303
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.11957{col 38}{space 2} .2452534{col 49}{space 1}   12.72{col 58}{space 3}0.000{col 66}{space 4}  2.60798{col 79}{space 3} 3.631159
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *MS Table 3b using OLS, Ordered Probit with clustered standard errors at the neighborhood level
. 
. reg revdalpha ib2.txt2, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 20)          =  {res}    10.31
                                                {txt}Prob > F          = {res}    0.0008
                                                {txt}R-squared         = {res}    0.1737
                                                {txt}Root MSE          =    {res} .88167

{txt}{ralign 83:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        revdalpha{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}txt2 {c |}
{space 3}Power-sharing  {c |}{col 19}{res}{space 2}-.7778448{col 31}{space 2} .2552168{col 42}{space 1}   -3.05{col 51}{space 3}0.006{col 59}{space 4}-1.310218{col 72}{space 3}-.2454719
{txt}Hashd power-grab  {c |}{col 19}{res}{space 2}-.9181481{col 31}{space 2} .2249768{col 42}{space 1}   -4.08{col 51}{space 3}0.001{col 59}{space 4}-1.387442{col 72}{space 3}-.4488548
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} .0914286{col 31}{space 2} .0534838{col 42}{space 1}    1.71{col 51}{space 3}0.103{col 59}{space 4}-.0201367{col 72}{space 3} .2029939
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. oprobit revdalpha ib2.txt2, cluster(locale)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1413.7621}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -1371.59}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1371.544}  
Iteration 3:{space 3}log pseudolikelihood = {res: -1371.544}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}     12.07
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0024
{txt}Log pseudolikelihood = {res} -1371.544{txt}{col 49}Pseudo R2{col 67}= {res}    0.0299

{txt}{ralign 83:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        revdalpha{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}txt2 {c |}
{space 3}Power-sharing  {c |}{col 19}{res}{space 2}-.8009799{col 31}{space 2} .3105961{col 42}{space 1}   -2.58{col 51}{space 3}0.010{col 59}{space 4}-1.409737{col 72}{space 3}-.1922228
{txt}Hashd power-grab  {c |}{col 19}{res}{space 2}-.9766487{col 31}{space 2} .2987184{col 42}{space 1}   -3.27{col 51}{space 3}0.001{col 59}{space 4}-1.562126{col 72}{space 3}-.3911715
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-3.725493{col 31}{space 2} .4178452{col 59}{space 4}-4.544455{col 72}{space 3}-2.906532
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-3.498949{col 31}{space 2} .3473226{col 59}{space 4}-4.179689{col 72}{space 3}-2.818209
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-2.673911{col 31}{space 2} .2037467{col 59}{space 4}-3.073247{col 72}{space 3}-2.274575
{txt}{space 12}/cut4 {c |}{col 19}{res}{space 2}-2.084532{col 31}{space 2} .1851337{col 59}{space 4}-2.447387{col 72}{space 3}-1.721676
{txt}{space 12}/cut5 {c |}{col 19}{res}{space 2}-1.740166{col 31}{space 2} .1515149{col 59}{space 4} -2.03713{col 72}{space 3}-1.443202
{txt}{space 12}/cut6 {c |}{col 19}{res}{space 2}-1.565113{col 31}{space 2} .1487919{col 59}{space 4} -1.85674{col 72}{space 3}-1.273486
{txt}{space 12}/cut7 {c |}{col 19}{res}{space 2}-1.246128{col 31}{space 2} .1336259{col 59}{space 4}-1.508029{col 72}{space 3}-.9842257
{txt}{space 12}/cut8 {c |}{col 19}{res}{space 2}-.8094079{col 31}{space 2} .1028749{col 59}{space 4}-1.011039{col 72}{space 3}-.6077769
{txt}{space 12}/cut9 {c |}{col 19}{res}{space 2}-.6768273{col 31}{space 2}  .085922{col 59}{space 4}-.8452313{col 72}{space 3}-.5084232
{txt}{space 11}/cut10 {c |}{col 19}{res}{space 2}-.5537721{col 31}{space 2} .0844896{col 59}{space 4}-.7193687{col 72}{space 3}-.3881755
{txt}{space 11}/cut11 {c |}{col 19}{res}{space 2}-.3202154{col 31}{space 2} .0754955{col 59}{space 4}-.4681838{col 72}{space 3}-.1722469
{txt}{space 11}/cut12 {c |}{col 19}{res}{space 2} .0209075{col 31}{space 2} .0677341{col 59}{space 4} -.111849{col 72}{space 3} .1536639
{txt}{space 11}/cut13 {c |}{col 19}{res}{space 2} .2501029{col 31}{space 2} .0864018{col 59}{space 4} .0807585{col 72}{space 3} .4194473
{txt}{space 11}/cut14 {c |}{col 19}{res}{space 2} .4497591{col 31}{space 2} .0841982{col 59}{space 4} .2847337{col 72}{space 3} .6147844
{txt}{space 11}/cut15 {c |}{col 19}{res}{space 2} 1.035378{col 31}{space 2} .1122639{col 59}{space 4} .8153453{col 72}{space 3} 1.255412
{txt}{space 11}/cut16 {c |}{col 19}{res}{space 2} 1.341467{col 31}{space 2} .1195982{col 59}{space 4} 1.107059{col 72}{space 3} 1.575875
{txt}{space 11}/cut17 {c |}{col 19}{res}{space 2} 1.761349{col 31}{space 2} .1853309{col 59}{space 4} 1.398107{col 72}{space 3} 2.124591
{txt}{space 11}/cut18 {c |}{col 19}{res}{space 2}  2.34444{col 31}{space 2} .2760904{col 59}{space 4} 1.803313{col 72}{space 3} 2.885567
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Demographic Balance Tests Across Treatment Groups
. 
. *Kolmogorov-Smirnov Balance Tests
. 
. ksmirnov gender, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.1200    0.034
{txt} Combined K-S:     {res}  0.1200    0.068

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov age, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0995    0.098
{txt} 1:                {res} -0.0263    0.850
{txt} Combined K-S:     {res}  0.0995    0.196

{txt}Note: Ties exist in combined dataset;
      there are 37 unique values out of 537 observations.

{com}. ksmirnov education, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0080    0.985
{txt} 1:                {res} -0.0497    0.561
{txt} Combined K-S:     {res}  0.0497    0.934

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov income, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0998    0.097
{txt} 1:                {res} -0.0478    0.585
{txt} Combined K-S:     {res}  0.0998    0.193

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov ethnicity, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0599    0.431
{txt} Combined K-S:     {res}  0.0599    0.795

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. ksmirnov religion, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0440    0.636
{txt} Combined K-S:     {res}  0.0440    0.977

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. 
. ksmirnov gender, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0381    0.700
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0381    0.994

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov age, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.2299    0.000
{txt} 1:                {res} -0.0055    0.993
{txt} Combined K-S:     {res}  0.2299    0.000

{txt}Note: Ties exist in combined dataset;
      there are 37 unique values out of 537 observations.

{com}. ksmirnov education, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0121    0.965
{txt} 1:                {res} -0.0327    0.770
{txt} Combined K-S:     {res}  0.0327    1.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov income, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.2175    0.000
{txt} Combined K-S:     {res}  0.2175    0.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov ethnicity, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0175    0.928
{txt} 1:                {res} -0.0086    0.982
{txt} Combined K-S:     {res}  0.0175    1.000

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. ksmirnov religion, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0683    0.319
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0683    0.617

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. 
. ksmirnov gender, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0797    0.224
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0797    0.442

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov age, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0057    0.992
{txt} 1:                {res} -0.2543    0.000
{txt} Combined K-S:     {res}  0.2543    0.000

{txt}Note: Ties exist in combined dataset;
      there are 37 unique values out of 537 observations.

{com}. ksmirnov education, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0368    0.727
{txt} 1:                {res} -0.0059    0.992
{txt} Combined K-S:     {res}  0.0368    0.997

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov income, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.2226    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.2226    0.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 537 observations.

{com}. ksmirnov ethnicity, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0413    0.668
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0413    0.988

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. ksmirnov religion, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0331    0.772
{txt} Combined K-S:     {res}  0.0331    1.000

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 537 observations.

{com}. 
. *Post-Treatment Security Expectations with Coarsened Exact Matching (OLS Regression)
. cem gender , treatment(powersharetxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}2
{txt}Number of matched strata: {res}2

           {txt}  0    1
      All  {res}364  173
{txt}  Matched  {res}364  173
{txt}Unmatched  {res}  0    0


{txt}Multivariate L1 distance: {res}6.939e-16

{txt}Univariate imbalance:

             L1     mean      min      25%      50%      75%      max
gender  {res}6.9e-16  5.6e-17        0        0        0        0        0
{txt}
{com}. reg revpostalpha i.txt2  [pweight=cem_weights], robust
{txt}(sum of wgt is 537)

Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}   102.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2208
                                                {txt}Root MSE          =    {res} .75768

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .7688357{col 38}{space 2} .0882786{col 49}{space 1}    8.71{col 58}{space 3}0.000{col 66}{space 4} .5954197{col 79}{space 3} .9422516
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.1537398{col 38}{space 2} .0885556{col 49}{space 1}   -1.74{col 58}{space 3}0.083{col 66}{space 4}-.3276999{col 79}{space 3} .0202203
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. cem age income , treatment(hashdpgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}38
{txt}Number of matched strata: {res}30

           {txt}  0    1
      All  {res}348  189
{txt}  Matched  {res}345  181
{txt}Unmatched  {res}  3    8


{txt}Multivariate L1 distance: {res}.11321996

{txt}Univariate imbalance:

              L1      mean       min       25%       50%       75%       max
   age  {res}  .07043   -.10089         0         0         0        -2         0
{txt}income  {res} 2.4e-16  -1.3e-15         0         0         0         0         0
{txt}
{com}. reg revpostalpha i.txt2  [pweight=cem_weights], robust
{txt}(sum of wgt is 526)

Linear regression                               Number of obs     = {res}       526
                                                {txt}F(2, 523)         =  {res}    33.72
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1760
                                                {txt}Root MSE          =    {res} .77044

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2} .7947294{col 38}{space 2} .1266112{col 49}{space 1}    6.28{col 58}{space 3}0.000{col 66}{space 4} .5460005{col 79}{space 3} 1.043458
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2} .0126461{col 38}{space 2} .1046952{col 49}{space 1}    0.12{col 58}{space 3}0.904{col 66}{space 4}-.1930287{col 79}{space 3}  .218321
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.082658{col 38}{space 2} .0932507{col 49}{space 1}   33.06{col 58}{space 3}0.000{col 66}{space 4} 2.899466{col 79}{space 3}  3.26585
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. cem age income , treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}38
{txt}Number of matched strata: {res}22

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}303  173
{txt}Unmatched  {res} 59    2


{txt}Multivariate L1 distance: {res}.08958513

{txt}Univariate imbalance:

             L1     mean      min      25%      50%      75%      max
   age  {res}  .0623  -.04415        0        0        1        0        0
{txt}income  {res}5.1e-16  3.6e-15        0        0        0        0        0
{txt}
{com}. reg revpostalpha i.txt2  [pweight=cem_weights], robust
{txt}(sum of wgt is 476)

Linear regression                               Number of obs     = {res}       476
                                                {txt}F(2, 473)         =  {res}    46.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1410
                                                {txt}Root MSE          =    {res} .77677

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}txt2 {c |}
Local Police power-grab  {c |}{col 26}{res}{space 2}  .451522{col 38}{space 2} .1139357{col 49}{space 1}    3.96{col 58}{space 3}0.000{col 66}{space 4} .2276392{col 79}{space 3} .6754048
{txt}{space 7}Hashd power-grab  {c |}{col 26}{res}{space 2}-.3254011{col 38}{space 2} .1250042{col 49}{space 1}   -2.60{col 58}{space 3}0.010{col 66}{space 4}-.5710333{col 79}{space 3}-.0797689
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.510906{col 38}{space 2} .1044666{col 49}{space 1}   33.61{col 58}{space 3}0.000{col 66}{space 4}  3.30563{col 79}{space 3} 3.716182
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Robustness Checks for MS Table 4 
. 
. *MS Table 4 (OLS with clustered standard errors at the neighborhood level)
. 
. reg revpostalpha ib3.txt , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 20)          =  {res}    15.91
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{ralign 90:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .2425702{col 49}{space 1}   -0.49{col 58}{space 3}0.629{col 66}{space 4}-.6248489{col 79}{space 3}  .387136
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .2523698{col 49}{space 1}    3.00{col 58}{space 3}0.007{col 66}{space 4} .2298417{col 79}{space 3}  1.28271
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .2374863{col 49}{space 1}   13.48{col 58}{space 3}0.000{col 66}{space 4} 2.705479{col 79}{space 3} 3.696255
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis ib3.txt##c.addvictimlibisis , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(8, 20)          =  {res}    37.95
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5199
                                                {txt}Root MSE          =    {res} .59549

{txt}{ralign 90:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-1.165996{col 38}{space 2} .2765502{col 49}{space 1}   -4.22{col 58}{space 3}0.000{col 66}{space 4}-1.742869{col 79}{space 3}-.5891219
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .301741{col 38}{space 2} .3277141{col 49}{space 1}    0.92{col 58}{space 3}0.368{col 66}{space 4}-.3818586{col 79}{space 3} .9853405
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.7281968{col 38}{space 2} .0890847{col 49}{space 1}   -8.17{col 58}{space 3}0.000{col 66}{space 4}-.9140243{col 79}{space 3}-.5423693
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .9858833{col 38}{space 2} .1579195{col 49}{space 1}    6.24{col 58}{space 3}0.000{col 66}{space 4} .6564689{col 79}{space 3} 1.315298
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .473976{col 38}{space 2} .1529483{col 49}{space 1}    3.10{col 58}{space 3}0.006{col 66}{space 4} .1549313{col 79}{space 3} .7930206
{txt}{space 24} {c |}
{space 8}addvictimlibisis {c |}{col 26}{res}{space 2} .0927452{col 38}{space 2} .0354468{col 49}{space 1}    2.62{col 58}{space 3}0.017{col 66}{space 4} .0188044{col 79}{space 3} .1666859
{txt}{space 24} {c |}
{space 2}txt#c.addvictimlibisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1106241{col 38}{space 2} .0432807{col 49}{space 1}    2.56{col 58}{space 3}0.019{col 66}{space 4} .0203421{col 79}{space 3} .2009061
{txt}Local police power grab  {c |}{col 26}{res}{space 2} -.092431{col 38}{space 2}  .050762{col 49}{space 1}   -1.82{col 58}{space 3}0.084{col 66}{space 4}-.1983186{col 79}{space 3} .0134566
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.717128{col 38}{space 2} .1376522{col 49}{space 1}   27.00{col 58}{space 3}0.000{col 66}{space 4} 3.429991{col 79}{space 3} 4.004266
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha  ib3.txt##c.addvictimair , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 20)          =  {res}    19.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2410
                                                {txt}Root MSE          =    {res} .74665

{txt}{ralign 90:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2373311{col 38}{space 2} .2552768{col 49}{space 1}   -0.93{col 58}{space 3}0.364{col 66}{space 4}-.7698292{col 79}{space 3}  .295167
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5905955{col 38}{space 2} .2556049{col 49}{space 1}    2.31{col 58}{space 3}0.032{col 66}{space 4}  .057413{col 79}{space 3} 1.123778
{txt}{space 24} {c |}
{space 12}addvictimair {c |}{col 26}{res}{space 2}-.6680743{col 38}{space 2}  .183859{col 49}{space 1}   -3.63{col 58}{space 3}0.002{col 66}{space 4}-1.051598{col 79}{space 3}-.2845511
{txt}{space 24} {c |}
{space 6}txt#c.addvictimair {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .5555578{col 38}{space 2} .3166468{col 49}{space 1}    1.75{col 58}{space 3}0.095{col 66}{space 4}-.1049559{col 79}{space 3} 1.216072
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .711439{col 38}{space 2} .1844161{col 49}{space 1}    3.86{col 58}{space 3}0.001{col 66}{space 4} .3267536{col 79}{space 3} 1.096124
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.34375{col 38}{space 2} .2399101{col 49}{space 1}   13.94{col 58}{space 3}0.000{col 66}{space 4} 2.843306{col 79}{space 3} 3.844194
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##crossfire , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 20)          =  {res}    19.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2455
                                                {txt}Root MSE          =    {res} .74442

{txt}{ralign 92:(Std. Err. adjusted for {res:21} clusters in locale)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}              revpostalpha{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}txt {c |}
{space 9}Hashd power grab  {c |}{col 28}{res}{space 2}-.2414072{col 40}{space 2} .2522057{col 51}{space 1}   -0.96{col 60}{space 3}0.350{col 68}{space 4}-.7674991{col 81}{space 3} .2846847
{txt}{space 2}Local police power grab  {c |}{col 28}{res}{space 2} .5792572{col 40}{space 2}   .25122{col 51}{space 1}    2.31{col 60}{space 3}0.032{col 68}{space 4} .0552216{col 81}{space 3} 1.103293
{txt}{space 26} {c |}
{space 15}1.crossfire {c |}{col 28}{res}{space 2}-.7263975{col 40}{space 2}  .185525{col 51}{space 1}   -3.92{col 60}{space 3}0.001{col 68}{space 4}-1.113396{col 81}{space 3}-.3393991
{txt}{space 26} {c |}
{space 13}txt#crossfire {c |}
{space 7}Hashd power grab#1  {c |}{col 28}{res}{space 2}  .613881{col 40}{space 2} .3173652{col 51}{space 1}    1.93{col 60}{space 3}0.067{col 68}{space 4}-.0481313{col 81}{space 3} 1.275893
{txt}Local police power grab#1  {c |}{col 28}{res}{space 2} .8220415{col 40}{space 2} .2015627{col 51}{space 1}    4.08{col 60}{space 3}0.001{col 68}{space 4} .4015891{col 81}{space 3} 1.242494
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.347826{col 40}{space 2} .2363309{col 51}{space 1}   14.17{col 60}{space 3}0.000{col 68}{space 4} 2.854848{col 81}{space 3} 3.840804
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Table 4. Victimization as a Post-Treatment Moderator of Security Expectations (OLS Regression, extended controls)
. 
. reg revpostalpha ib3.txt gender age education unemployed income i.religion i.ethnicity , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(11, 525)        =  {res}    30.86
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2905
                                                {txt}Root MSE          =    {res} .72602

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1252747{col 38}{space 2} .0856975{col 49}{space 1}   -1.46{col 58}{space 3}0.144{col 66}{space 4}-.2936268{col 79}{space 3} .0430774
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5855483{col 38}{space 2} .0888018{col 49}{space 1}    6.59{col 58}{space 3}0.000{col 66}{space 4} .4110979{col 79}{space 3} .7599988
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .1322718{col 38}{space 2} .0746921{col 49}{space 1}    1.77{col 58}{space 3}0.077{col 66}{space 4}-.0144602{col 79}{space 3} .2790039
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0201566{col 38}{space 2} .0044031{col 49}{space 1}   -4.58{col 58}{space 3}0.000{col 66}{space 4}-.0288064{col 79}{space 3}-.0115068
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1261097{col 38}{space 2} .0492663{col 49}{space 1}    2.56{col 58}{space 3}0.011{col 66}{space 4} .0293264{col 79}{space 3}  .222893
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .2192707{col 38}{space 2} .0753756{col 49}{space 1}    2.91{col 58}{space 3}0.004{col 66}{space 4} .0711959{col 79}{space 3} .3673455
{txt}{space 18}income {c |}{col 26}{res}{space 2} .0791167{col 38}{space 2} .0384909{col 49}{space 1}    2.06{col 58}{space 3}0.040{col 66}{space 4} .0035017{col 79}{space 3} .1547318
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .3519405{col 38}{space 2}  .191128{col 49}{space 1}    1.84{col 58}{space 3}0.066{col 66}{space 4} -.023529{col 79}{space 3}   .72741
{txt}{space 14}Christian  {c |}{col 26}{res}{space 2}  .248358{col 38}{space 2} .1260321{col 49}{space 1}    1.97{col 58}{space 3}0.049{col 66}{space 4} .0007688{col 79}{space 3} .4959472
{txt}{space 24} {c |}
{space 15}ethnicity {c |}
{space 19}Kurd  {c |}{col 26}{res}{space 2} .4179284{col 38}{space 2} .1100442{col 49}{space 1}    3.80{col 58}{space 3}0.000{col 66}{space 4} .2017473{col 79}{space 3} .6341094
{txt}{space 16}Turkmen  {c |}{col 26}{res}{space 2} .2179062{col 38}{space 2} .4029907{col 49}{space 1}    0.54{col 58}{space 3}0.589{col 66}{space 4}-.5737661{col 79}{space 3} 1.009578
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.11957{col 38}{space 2} .1907893{col 49}{space 1}   16.35{col 58}{space 3}0.000{col 66}{space 4} 2.744765{col 79}{space 3} 3.494374
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis ib3.txt##c.addvictimlibisis gender age education unemployed income i.religion i.ethnicity , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(17, 519)        =  {res}    54.04
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5801
                                                {txt}Root MSE          =    {res} .56174

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-1.304168{col 38}{space 2} .1573149{col 49}{space 1}   -8.29{col 58}{space 3}0.000{col 66}{space 4} -1.61322{col 79}{space 3} -.995116
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .1039777{col 38}{space 2} .3123684{col 49}{space 1}    0.33{col 58}{space 3}0.739{col 66}{space 4}-.5096842{col 79}{space 3} .7176397
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.6921368{col 38}{space 2} .0750754{col 49}{space 1}   -9.22{col 58}{space 3}0.000{col 66}{space 4}-.8396259{col 79}{space 3}-.5446477
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} 1.056202{col 38}{space 2}   .09519{col 49}{space 1}   11.10{col 58}{space 3}0.000{col 66}{space 4} .8691967{col 79}{space 3} 1.243207
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .4674142{col 38}{space 2} .1050403{col 49}{space 1}    4.45{col 58}{space 3}0.000{col 66}{space 4} .2610577{col 79}{space 3} .6737707
{txt}{space 24} {c |}
{space 8}addvictimlibisis {c |}{col 26}{res}{space 2} .1107613{col 38}{space 2} .0376801{col 49}{space 1}    2.94{col 58}{space 3}0.003{col 66}{space 4}  .036737{col 79}{space 3} .1847855
{txt}{space 24} {c |}
{space 2}txt#c.addvictimlibisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1216156{col 38}{space 2} .0417177{col 49}{space 1}    2.92{col 58}{space 3}0.004{col 66}{space 4} .0396594{col 79}{space 3} .2035719
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.0887995{col 38}{space 2}  .052511{col 49}{space 1}   -1.69{col 58}{space 3}0.091{col 66}{space 4}-.1919597{col 79}{space 3} .0143607
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} -.062237{col 38}{space 2} .0506594{col 49}{space 1}   -1.23{col 58}{space 3}0.220{col 66}{space 4}-.1617598{col 79}{space 3} .0372858
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0147739{col 38}{space 2} .0034058{col 49}{space 1}   -4.34{col 58}{space 3}0.000{col 66}{space 4}-.0214647{col 79}{space 3}-.0080831
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0885312{col 38}{space 2} .0425509{col 49}{space 1}    2.08{col 58}{space 3}0.038{col 66}{space 4}  .004938{col 79}{space 3} .1721244
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .0029784{col 38}{space 2} .0545521{col 49}{space 1}    0.05{col 58}{space 3}0.956{col 66}{space 4}-.1041917{col 79}{space 3} .1101484
{txt}{space 18}income {c |}{col 26}{res}{space 2} .0426917{col 38}{space 2} .0275427{col 49}{space 1}    1.55{col 58}{space 3}0.122{col 66}{space 4}-.0114171{col 79}{space 3} .0968006
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .3033303{col 38}{space 2} .1081118{col 49}{space 1}    2.81{col 58}{space 3}0.005{col 66}{space 4} .0909397{col 79}{space 3} .5157209
{txt}{space 14}Christian  {c |}{col 26}{res}{space 2} .6288107{col 38}{space 2} .1763223{col 49}{space 1}    3.57{col 58}{space 3}0.000{col 66}{space 4} .2824176{col 79}{space 3} .9752039
{txt}{space 24} {c |}
{space 15}ethnicity {c |}
{space 19}Kurd  {c |}{col 26}{res}{space 2}  .410691{col 38}{space 2} .1646202{col 49}{space 1}    2.49{col 58}{space 3}0.013{col 66}{space 4} .0872872{col 79}{space 3} .7340949
{txt}{space 16}Turkmen  {c |}{col 26}{res}{space 2} .7964434{col 38}{space 2} .6397489{col 49}{space 1}    1.24{col 58}{space 3}0.214{col 66}{space 4}-.4603723{col 79}{space 3} 2.053259
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.715559{col 38}{space 2} .2071204{col 49}{space 1}   17.94{col 58}{space 3}0.000{col 66}{space 4} 3.308662{col 79}{space 3} 4.122456
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha  ib3.txt##c.addvictimair gender age education unemployed income i.religion i.ethnicity  , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(14, 522)        =  {res}    29.80
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3220
                                                {txt}Root MSE          =    {res} .71175

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2248267{col 38}{space 2} .1017562{col 49}{space 1}   -2.21{col 58}{space 3}0.028{col 66}{space 4}-.4247287{col 79}{space 3}-.0249247
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .4565061{col 38}{space 2} .1001493{col 49}{space 1}    4.56{col 58}{space 3}0.000{col 66}{space 4} .2597609{col 79}{space 3} .6532513
{txt}{space 24} {c |}
{space 12}addvictimair {c |}{col 26}{res}{space 2}-.6393491{col 38}{space 2} .1273231{col 49}{space 1}   -5.02{col 58}{space 3}0.000{col 66}{space 4}-.8894777{col 79}{space 3}-.3892205
{txt}{space 24} {c |}
{space 6}txt#c.addvictimair {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .4728067{col 38}{space 2} .1913415{col 49}{space 1}    2.47{col 58}{space 3}0.014{col 66}{space 4} .0969128{col 79}{space 3} .8487007
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .6348269{col 38}{space 2} .1332733{col 49}{space 1}    4.76{col 58}{space 3}0.000{col 66}{space 4}  .373009{col 79}{space 3} .8966448
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .0852255{col 38}{space 2} .0758272{col 49}{space 1}    1.12{col 58}{space 3}0.262{col 66}{space 4}-.0637384{col 79}{space 3} .2341894
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0220121{col 38}{space 2} .0046033{col 49}{space 1}   -4.78{col 58}{space 3}0.000{col 66}{space 4}-.0310554{col 79}{space 3}-.0129688
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1334643{col 38}{space 2} .0495819{col 49}{space 1}    2.69{col 58}{space 3}0.007{col 66}{space 4} .0360596{col 79}{space 3} .2308689
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .2023493{col 38}{space 2}  .072892{col 49}{space 1}    2.78{col 58}{space 3}0.006{col 66}{space 4} .0591515{col 79}{space 3} .3455471
{txt}{space 18}income {c |}{col 26}{res}{space 2} .0610474{col 38}{space 2} .0389679{col 49}{space 1}    1.57{col 58}{space 3}0.118{col 66}{space 4}-.0155058{col 79}{space 3} .1376005
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .3798063{col 38}{space 2} .1982273{col 49}{space 1}    1.92{col 58}{space 3}0.056{col 66}{space 4} -.009615{col 79}{space 3} .7692276
{txt}{space 14}Christian  {c |}{col 26}{res}{space 2} .2339056{col 38}{space 2} .1320335{col 49}{space 1}    1.77{col 58}{space 3}0.077{col 66}{space 4}-.0254767{col 79}{space 3}  .493288
{txt}{space 24} {c |}
{space 15}ethnicity {c |}
{space 19}Kurd  {c |}{col 26}{res}{space 2} .4119895{col 38}{space 2} .1149583{col 49}{space 1}    3.58{col 58}{space 3}0.000{col 66}{space 4} .1861517{col 79}{space 3} .6378273
{txt}{space 16}Turkmen  {c |}{col 26}{res}{space 2} .2294516{col 38}{space 2} .3892275{col 49}{space 1}    0.59{col 58}{space 3}0.556{col 66}{space 4}-.5351932{col 79}{space 3} .9940964
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.349134{col 38}{space 2} .2064933{col 49}{space 1}   16.22{col 58}{space 3}0.000{col 66}{space 4} 2.943474{col 79}{space 3} 3.754794
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##crossfire gender age education unemployed income i.religion i.ethnicity , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(14, 522)        =  {res}    31.13
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3259
                                                {txt}Root MSE          =    {res}  .7097

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}              revpostalpha{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}txt {c |}
{space 9}Hashd power grab  {c |}{col 28}{res}{space 2}-.2276489{col 40}{space 2} .1010115{col 51}{space 1}   -2.25{col 60}{space 3}0.025{col 68}{space 4}-.4260879{col 81}{space 3}-.0292099
{txt}{space 2}Local police power grab  {c |}{col 28}{res}{space 2}  .443674{col 40}{space 2}  .101178{col 51}{space 1}    4.39{col 60}{space 3}0.000{col 68}{space 4}  .244908{col 81}{space 3} .6424401
{txt}{space 26} {c |}
{space 15}1.crossfire {c |}{col 28}{res}{space 2} -.693461{col 40}{space 2} .1225094{col 51}{space 1}   -5.66{col 60}{space 3}0.000{col 68}{space 4}-.9341329{col 81}{space 3} -.452789
{txt}{space 26} {c |}
{space 13}txt#crossfire {c |}
{space 7}Hashd power grab#1  {c |}{col 28}{res}{space 2}  .524413{col 40}{space 2} .1878158{col 51}{space 1}    2.79{col 60}{space 3}0.005{col 68}{space 4} .1554453{col 81}{space 3} .8933806
{txt}Local police power grab#1  {c |}{col 28}{res}{space 2} .7219809{col 40}{space 2} .1604052{col 51}{space 1}    4.50{col 60}{space 3}0.000{col 68}{space 4} .4068618{col 81}{space 3}   1.0371
{txt}{space 26} {c |}
{space 20}gender {c |}{col 28}{res}{space 2} .0829272{col 40}{space 2} .0752689{col 51}{space 1}    1.10{col 60}{space 3}0.271{col 68}{space 4}  -.06494{col 81}{space 3} .2307944
{txt}{space 23}age {c |}{col 28}{res}{space 2}-.0220359{col 40}{space 2} .0045952{col 51}{space 1}   -4.80{col 60}{space 3}0.000{col 68}{space 4}-.0310632{col 81}{space 3}-.0130086
{txt}{space 17}education {c |}{col 28}{res}{space 2} .1282806{col 40}{space 2}  .048783{col 51}{space 1}    2.63{col 60}{space 3}0.009{col 68}{space 4} .0324455{col 81}{space 3} .2241157
{txt}{space 16}unemployed {c |}{col 28}{res}{space 2} .2014821{col 40}{space 2} .0729067{col 51}{space 1}    2.76{col 60}{space 3}0.006{col 68}{space 4} .0582556{col 81}{space 3} .3447086
{txt}{space 20}income {c |}{col 28}{res}{space 2}  .060549{col 40}{space 2} .0386395{col 51}{space 1}    1.57{col 60}{space 3}0.118{col 68}{space 4} -.015359{col 81}{space 3}  .136457
{txt}{space 26} {c |}
{space 18}religion {c |}
{space 21}Shia  {c |}{col 28}{res}{space 2} .3655324{col 40}{space 2} .2070741{col 51}{space 1}    1.77{col 60}{space 3}0.078{col 68}{space 4}-.0412686{col 81}{space 3} .7723333
{txt}{space 16}Christian  {c |}{col 28}{res}{space 2} .2495344{col 40}{space 2} .1352051{col 51}{space 1}    1.85{col 60}{space 3}0.066{col 68}{space 4}-.0160785{col 81}{space 3} .5151473
{txt}{space 26} {c |}
{space 17}ethnicity {c |}
{space 21}Kurd  {c |}{col 28}{res}{space 2} .3962163{col 40}{space 2} .1183182{col 51}{space 1}    3.35{col 60}{space 3}0.001{col 68}{space 4}  .163778{col 81}{space 3} .6286547
{txt}{space 18}Turkmen  {c |}{col 28}{res}{space 2} .2130525{col 40}{space 2} .4044655{col 51}{space 1}    0.53{col 60}{space 3}0.599{col 68}{space 4}-.5815276{col 81}{space 3} 1.007633
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.372702{col 40}{space 2}  .207722{col 51}{space 1}   16.24{col 60}{space 3}0.000{col 68}{space 4} 2.964628{col 81}{space 3} 3.780776
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Internal Validity of the Experiment
. 
. *Analysis of Power-sharing vs. Power-grabbing Treatment-Effect Covariates (OLS Regression)
. 
. reg revpostalpha ib3.txt , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revbaghdadpower revmosulpower revpowershare , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    89.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4265
                                                {txt}Root MSE          =    {res} .64904

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1871937{col 38}{space 2} .0746416{col 49}{space 1}   -2.51{col 58}{space 3}0.012{col 66}{space 4}-.3338227{col 79}{space 3}-.0405646
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.0101474{col 38}{space 2} .1031741{col 49}{space 1}   -0.10{col 58}{space 3}0.922{col 66}{space 4}-.2128269{col 79}{space 3} .1925321
{txt}{space 24} {c |}
{space 9}revbaghdadpower {c |}{col 26}{res}{space 2} .2875384{col 38}{space 2} .0359945{col 49}{space 1}    7.99{col 58}{space 3}0.000{col 66}{space 4} .2168292{col 79}{space 3} .3582475
{txt}{space 11}revmosulpower {c |}{col 26}{res}{space 2}-.1331129{col 38}{space 2} .0461657{col 49}{space 1}   -2.88{col 58}{space 3}0.004{col 66}{space 4}-.2238027{col 79}{space 3}-.0424231
{txt}{space 11}revpowershare {c |}{col 26}{res}{space 2} .3061046{col 38}{space 2} .0367031{col 49}{space 1}    8.34{col 58}{space 3}0.000{col 66}{space 4} .2340035{col 79}{space 3} .3782057
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.456571{col 38}{space 2} .1551299{col 49}{space 1}   15.84{col 58}{space 3}0.000{col 66}{space 4} 2.151828{col 79}{space 3} 2.761315
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Power-sharing/Power-grabbing Preferences and Security Expectations
. 
. reg revpostalpha ib3.txt , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revbaghdadpower , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   125.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3486
                                                {txt}Root MSE          =    {res}  .6904

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1834057{col 38}{space 2} .0792465{col 49}{space 1}   -2.31{col 58}{space 3}0.021{col 66}{space 4}-.3390794{col 79}{space 3} -.027732
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .3536571{col 38}{space 2}  .092724{col 49}{space 1}    3.81{col 58}{space 3}0.000{col 66}{space 4} .1715078{col 79}{space 3} .5358064
{txt}{space 24} {c |}
{space 9}revbaghdadpower {c |}{col 26}{res}{space 2} .3987465{col 38}{space 2} .0333559{col 49}{space 1}   11.95{col 58}{space 3}0.000{col 66}{space 4} .3332214{col 79}{space 3} .4642716
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.343447{col 38}{space 2} .0983691{col 49}{space 1}   23.82{col 58}{space 3}0.000{col 66}{space 4} 2.150208{col 79}{space 3} 2.536685
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revmosulpower , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}    66.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2106
                                                {txt}Root MSE          =    {res} .76004

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} -.126943{col 38}{space 2} .0879707{col 49}{space 1}   -1.44{col 58}{space 3}0.150{col 66}{space 4}-.2997549{col 79}{space 3} .0458689
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7164956{col 38}{space 2} .0883081{col 49}{space 1}    8.11{col 58}{space 3}0.000{col 66}{space 4} .5430209{col 79}{space 3} .8899703
{txt}{space 24} {c |}
{space 11}revmosulpower {c |}{col 26}{res}{space 2} -.091648{col 38}{space 2} .0531371{col 49}{space 1}   -1.72{col 58}{space 3}0.085{col 66}{space 4}-.1960318{col 79}{space 3} .0127358
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.519781{col 38}{space 2} .1851212{col 49}{space 1}   19.01{col 58}{space 3}0.000{col 66}{space 4} 3.156124{col 79}{space 3} 3.883438
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revpowershare , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   137.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3581
                                                {txt}Root MSE          =    {res} .68537

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1326548{col 38}{space 2} .0753331{col 49}{space 1}   -1.76{col 58}{space 3}0.079{col 66}{space 4} -.280641{col 79}{space 3} .0153313
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .1816722{col 38}{space 2} .1021995{col 49}{space 1}    1.78{col 58}{space 3}0.076{col 66}{space 4} -.019091{col 79}{space 3} .3824355
{txt}{space 24} {c |}
{space 11}revpowershare {c |}{col 26}{res}{space 2} .4204704{col 38}{space 2} .0350037{col 49}{space 1}   12.01{col 58}{space 3}0.000{col 66}{space 4} .3517084{col 79}{space 3} .4892325
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.391522{col 38}{space 2} .0893546{col 49}{space 1}   26.76{col 58}{space 3}0.000{col 66}{space 4} 2.215992{col 79}{space 3} 2.567053
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Analysis of Other Potential Treatment Moderators/Covariates
. 
. *Demographic Correlates of Security
. 
. reg revpostalpha ib3.txt gender age education i.typeofwork income i.religion , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}{help j_robustsingular:F(15, 519) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.3169
                                                {txt}Root MSE          =    {res} .71649

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             revpostalpha{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}txt {c |}
{space 8}Hashd power grab  {c |}{col 27}{res}{space 2}-.1326962{col 39}{space 2} .0879708{col 50}{space 1}   -1.51{col 59}{space 3}0.132{col 67}{space 4}-.3055189{col 80}{space 3} .0401265
{txt}{space 1}Local police power grab  {c |}{col 27}{res}{space 2} .5506675{col 39}{space 2} .0905531{col 50}{space 1}    6.08{col 59}{space 3}0.000{col 67}{space 4} .3727718{col 80}{space 3} .7285632
{txt}{space 25} {c |}
{space 19}gender {c |}{col 27}{res}{space 2} .1800568{col 39}{space 2} .0741071{col 50}{space 1}    2.43{col 59}{space 3}0.015{col 67}{space 4} .0344701{col 80}{space 3} .3256434
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0207923{col 39}{space 2} .0053587{col 50}{space 1}   -3.88{col 59}{space 3}0.000{col 67}{space 4}-.0313197{col 80}{space 3}-.0102649
{txt}{space 16}education {c |}{col 27}{res}{space 2}    .2561{col 39}{space 2} .0592337{col 50}{space 1}    4.32{col 59}{space 3}0.000{col 67}{space 4} .1397327{col 80}{space 3} .3724673
{txt}{space 25} {c |}
{space 15}typeofwork {c |}
{space 5}professional worker  {c |}{col 27}{res}{space 2} .3113937{col 39}{space 2} .1247964{col 50}{space 1}    2.50{col 59}{space 3}0.013{col 67}{space 4} .0662255{col 80}{space 3}  .556562
{txt}office worker, secretary  {c |}{col 27}{res}{space 2} .3895645{col 39}{space 2} .1174833{col 50}{space 1}    3.32{col 59}{space 3}0.001{col 67}{space 4} .1587632{col 80}{space 3} .6203659
{txt}{space 11}manual worker  {c |}{col 27}{res}{space 2} .6741468{col 39}{space 2} .1256886{col 50}{space 1}    5.36{col 59}{space 3}0.000{col 67}{space 4} .4272258{col 80}{space 3} .9210678
{txt}{space 5}agricultural worker  {c |}{col 27}{res}{space 2} .5850823{col 39}{space 2} .1303205{col 50}{space 1}    4.49{col 59}{space 3}0.000{col 67}{space 4} .3290618{col 80}{space 3} .8411029
{txt}{space 2}armed forces, security  {c |}{col 27}{res}{space 2}  .657537{col 39}{space 2} .2316586{col 50}{space 1}    2.84{col 59}{space 3}0.005{col 67}{space 4} .2024332{col 80}{space 3} 1.112641
{txt}{space 14}unemployed  {c |}{col 27}{res}{space 2} .6755256{col 39}{space 2} .1194479{col 50}{space 1}    5.66{col 59}{space 3}0.000{col 67}{space 4} .4408648{col 80}{space 3} .9101863
{txt}{space 17}student  {c |}{col 27}{res}{space 2}  .401952{col 39}{space 2} .1383179{col 50}{space 1}    2.91{col 59}{space 3}0.004{col 67}{space 4} .1302202{col 80}{space 3} .6736838
{txt}{space 15}pensioner  {c |}{col 27}{res}{space 2} .8983921{col 39}{space 2} .2362164{col 50}{space 1}    3.80{col 59}{space 3}0.000{col 67}{space 4} .4343343{col 80}{space 3}  1.36245
{txt}{space 19}other  {c |}{col 27}{res}{space 2} 1.084557{col 39}{space 2}  .242879{col 50}{space 1}    4.47{col 59}{space 3}0.000{col 67}{space 4} .6074104{col 80}{space 3} 1.561704
{txt}{space 25} {c |}
{space 19}income {c |}{col 27}{res}{space 2} .0838903{col 39}{space 2}  .039447{col 50}{space 1}    2.13{col 59}{space 3}0.034{col 67}{space 4}  .006395{col 80}{space 3} .1613857
{txt}{space 25} {c |}
{space 17}religion {c |}
{space 20}Shia  {c |}{col 27}{res}{space 2} .3315066{col 39}{space 2} .2126337{col 50}{space 1}    1.56{col 59}{space 3}0.120{col 67}{space 4} -.086222{col 80}{space 3} .7492352
{txt}{space 15}Christian  {c |}{col 27}{res}{space 2} .6375971{col 39}{space 2} .0904852{col 50}{space 1}    7.05{col 59}{space 3}0.000{col 67}{space 4} .4598347{col 80}{space 3} .8153594
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 2.284607{col 39}{space 2} .2828952{col 50}{space 1}    8.08{col 59}{space 3}0.000{col 67}{space 4} 1.728846{col 80}{space 3} 2.840367
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Victimization during ISIS occupation (2014-2017)
. 
. sum punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fleehomeisis lootedisis womenabusedisis 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
punishedisis {c |}{res}        537    .4674115    .4994021          0          1
{txt}fampunishe~s {c |}{res}        537    .0856611    .2801239          0          1
{txt}{space 1}injuredisis {c |}{res}        537    .0018622    .0431532          0          1
{txt}faminj~disis {c |}{res}        537     .027933    .1649344          0          1
{txt}famkilledi~s {c |}{res}        537    .0446927    .2068211          0          1
{txt}{hline 13}{c +}{hline 57}
impris~disis {c |}{res}        537    .0912477    .2882295          0          1
{txt}fleehomeisis {c |}{res}        537    .0223464    .1479452          0          1
{txt}{space 2}lootedisis {c |}{res}        537    .0633147    .2437552          0          1
{txt}womena~disis {c |}{res}        537    .0148976     .121256          0          1
{txt}
{com}. 
. *Factor Analysis of Victimization during ISIS Occupation
. 
. factor punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fleehomeisis lootedisis womenabusedisis 
{txt}(obs=537)

Factor analysis/correlation{col 50}Number of obs    = {res}       537
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       4
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      30

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.20944      0.73719            0.8706       0.8706
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.47226      0.15507            0.3399       1.2105
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.31719      0.14457            0.2283       1.4388
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.17261      0.18784            0.1242       1.5630
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.01523      0.06142           -0.0110       1.5521
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.07665      0.01974           -0.0552       1.4969
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.09638      0.16235           -0.0694       1.4275
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.25873      0.07651           -0.1862       1.2413
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.33524            .           -0.2413       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}36{txt}) ={res}  428.99{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:punishedisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4126}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0165}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1307}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2519}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7489}}}{space 1}
{space 4}{space 0}{ralign 12:fampunishe~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4728}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1925}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2650}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0906}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6610}}}{space 1}
{space 4}{space 0}{ralign 12:injuredisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0173}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3072}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2910}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0035}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8206}}}{space 1}
{space 4}{space 0}{ralign 12:faminj~disis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2856}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0912}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0467}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2273}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8563}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4077}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0571}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0675}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0225}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8255}}}{space 1}
{space 4}{space 0}{ralign 12:impris~disis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4530}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1467}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1948}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0674}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7308}}}{space 1}
{space 4}{space 0}{ralign 12:fleehomeisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0591}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4314}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1997}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0399}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7690}}}{space 1}
{space 4}{space 0}{ralign 12:lootedisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0037}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3481}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2452}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1064}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8074}}}{space 1}
{space 4}{space 0}{ralign 12:womena~disis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5990}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0151}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0222}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1770}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6091}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Figure 6. ISIS Victimization Histogram
. 
. histogram addvictimisis , discrete percent addlabel
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. *ISIS Victimization and Treatment Effects (OLS regression)
. 
. reg revpostalpha ib3.txt , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt i.punishedisis i.fampunishedisis i.injuredisis i.faminjuredisis i.famkilledisis i.imprisonedisis i.fleehomeisis i.lootedisis i.womenabusedisis , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}{help j_robustsingular:F(10, 525) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.4939
                                                {txt}Root MSE          =    {res} .61318

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1094115{col 38}{space 2} .0744248{col 49}{space 1}    1.47{col 58}{space 3}0.142{col 66}{space 4}-.0367955{col 79}{space 3} .2556184
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .1640877{col 38}{space 2} .0890597{col 49}{space 1}    1.84{col 58}{space 3}0.066{col 66}{space 4}-.0108695{col 79}{space 3} .3390449
{txt}{space 24} {c |}
{space 10}1.punishedisis {c |}{col 26}{res}{space 2}-1.064828{col 38}{space 2}  .086876{col 49}{space 1}  -12.26{col 58}{space 3}0.000{col 66}{space 4}-1.235495{col 79}{space 3}-.8941605
{txt}{space 7}1.fampunishedisis {c |}{col 26}{res}{space 2}-.1034849{col 38}{space 2} .1284911{col 49}{space 1}   -0.81{col 58}{space 3}0.421{col 66}{space 4}-.3559047{col 79}{space 3} .1489349
{txt}{space 11}1.injuredisis {c |}{col 26}{res}{space 2} 1.791418{col 38}{space 2}  .398812{col 49}{space 1}    4.49{col 58}{space 3}0.000{col 66}{space 4} 1.007954{col 79}{space 3} 2.574881
{txt}{space 8}1.faminjuredisis {c |}{col 26}{res}{space 2}-.7442856{col 38}{space 2} .2369853{col 49}{space 1}   -3.14{col 58}{space 3}0.002{col 66}{space 4}-1.209842{col 79}{space 3}-.2787297
{txt}{space 9}1.famkilledisis {c |}{col 26}{res}{space 2}-.0493674{col 38}{space 2}  .115352{col 49}{space 1}   -0.43{col 58}{space 3}0.669{col 66}{space 4}-.2759756{col 79}{space 3} .1772407
{txt}{space 8}1.imprisonedisis {c |}{col 26}{res}{space 2}-.0478134{col 38}{space 2} .0929902{col 49}{space 1}   -0.51{col 58}{space 3}0.607{col 66}{space 4}-.2304919{col 79}{space 3} .1348651
{txt}{space 10}1.fleehomeisis {c |}{col 26}{res}{space 2}-.9494504{col 38}{space 2} .3872596{col 49}{space 1}   -2.45{col 58}{space 3}0.015{col 66}{space 4}-1.710219{col 79}{space 3}-.1886817
{txt}{space 12}1.lootedisis {c |}{col 26}{res}{space 2}-.0302367{col 38}{space 2} .1900785{col 49}{space 1}   -0.16{col 58}{space 3}0.874{col 66}{space 4}-.4036445{col 79}{space 3} .3431711
{txt}{space 7}1.womenabusedisis {c |}{col 26}{res}{space 2} .2547178{col 38}{space 2} .5230151{col 49}{space 1}    0.49{col 58}{space 3}0.626{col 66}{space 4}-.7727417{col 79}{space 3} 1.282177
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.863449{col 38}{space 2} .0817474{col 49}{space 1}   47.26{col 58}{space 3}0.000{col 66}{space 4} 3.702857{col 79}{space 3} 4.024042
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt addvictimisis , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   124.88
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3678
                                                {txt}Root MSE          =    {res} .68017

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0280479{col 38}{space 2} .0787893{col 49}{space 1}    0.36{col 58}{space 3}0.722{col 66}{space 4}-.1267277{col 79}{space 3} .1828236
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5065687{col 38}{space 2} .0932672{col 49}{space 1}    5.43{col 58}{space 3}0.000{col 66}{space 4} .3233523{col 79}{space 3}  .689785
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2} -.387428{col 38}{space 2} .0581012{col 49}{space 1}   -6.67{col 58}{space 3}0.000{col 66}{space 4}-.5015634{col 79}{space 3}-.2732927
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.547985{col 38}{space 2} .0833644{col 49}{space 1}   42.56{col 58}{space 3}0.000{col 66}{space 4} 3.384222{col 79}{space 3} 3.711748
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    90.28
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4394
                                                {txt}Root MSE          =    {res} .64166

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.6062244{col 38}{space 2}  .144788{col 49}{space 1}   -4.19{col 58}{space 3}0.000{col 66}{space 4} -.890652{col 79}{space 3}-.3217969
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .1107315{col 38}{space 2} .0907773{col 49}{space 1}    1.22{col 58}{space 3}0.223{col 66}{space 4}-.0675952{col 79}{space 3} .2890582
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.7919474{col 38}{space 2} .0721234{col 49}{space 1}  -10.98{col 58}{space 3}0.000{col 66}{space 4}-.9336295{col 79}{space 3}-.6502652
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .6177064{col 38}{space 2} .1066493{col 49}{space 1}    5.79{col 58}{space 3}0.000{col 66}{space 4} .4082002{col 79}{space 3} .8272127
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5373863{col 38}{space 2} .1155022{col 49}{space 1}    4.65{col 58}{space 3}0.000{col 66}{space 4}  .310489{col 79}{space 3} .7642836
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.910415{col 38}{space 2}  .083619{col 49}{space 1}   46.76{col 58}{space 3}0.000{col 66}{space 4} 3.746151{col 79}{space 3}  4.07468
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Balance Tests on ISIS Victimization Pre-Liberation
. 
. ksmirnov punishedisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.1206    0.033
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.1206    0.066

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fampunishedisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0015    0.999
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0015    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov injuredisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0027    0.998
{txt} Combined K-S:     {res}  0.0027    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0355    0.744
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0355    0.998

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0023    0.999
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0023    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0104    0.975
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0104    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomeisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0011    1.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0011    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov lootedisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0337    0.766
{txt} Combined K-S:     {res}  0.0337    0.999

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0220    0.893
{txt} Combined K-S:     {res}  0.0220    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov punishedisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.4789    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.4789    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fampunishedisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0474    0.576
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0474    0.946

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov injuredisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0053    0.993
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0053    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0186    0.919
{txt} Combined K-S:     {res}  0.0186    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0208    0.899
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0208    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0551    0.475
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0551    0.850

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomeisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0227    0.882
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0227    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov lootedisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.1146    0.040
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.1146    0.080

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0230    0.879
{txt} Combined K-S:     {res}  0.0230    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov punishedisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.6171    0.000
{txt} Combined K-S:     {res}  0.6171    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fampunishedisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0508    0.544
{txt} Combined K-S:     {res}  0.0508    0.921

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov injuredisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0028    0.998
{txt} Combined K-S:     {res}  0.0028    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0160    0.941
{txt} Combined K-S:     {res}  0.0160    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0239    0.874
{txt} Combined K-S:     {res}  0.0239    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0675    0.341
{txt} Combined K-S:     {res}  0.0675    0.655

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomeisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0247    0.866
{txt} Combined K-S:     {res}  0.0247    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov lootedisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0854    0.179
{txt} Combined K-S:     {res}  0.0854    0.355

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0457    0.611
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0457    0.966

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. *Figure 7. ISIS Victimization by Treatment
. 
. cibar addvictimisis, over1(txt2)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 7 bar isis victimization by txt.grec" file with the command graph play " Figure 7 bar isis victimization by txt.grec"
. 
. *Treatment Effects With Coarsened Exact Matching on ISIS Victimization Pre-Liberation (OLS Regression)
. 
. cem punishedisis, treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}2
{txt}Number of matched strata: {res}2

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}362  175
{txt}Unmatched  {res}  0    0


{txt}Multivariate L1 distance: {res}2.980e-15

{txt}Univariate imbalance:

                   L1     mean      min      25%      50%      75%      max
punishedisis  {res}3.0e-15  5.2e-16        0        0        0        0        0
{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis [pweight=cem_weights], robust
{txt}(sum of wgt is 537)

Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    41.10
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4035
                                                {txt}Root MSE          =    {res} .63092

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1028201{col 38}{space 2} .1270477{col 49}{space 1}   -0.81{col 58}{space 3}0.419{col 66}{space 4} -.352398{col 79}{space 3} .1467577
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.0891369{col 38}{space 2} .0919367{col 49}{space 1}   -0.97{col 58}{space 3}0.333{col 66}{space 4}-.2697412{col 79}{space 3} .0914673
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-1.211169{col 38}{space 2} .0984914{col 49}{space 1}  -12.30{col 58}{space 3}0.000{col 66}{space 4}-1.404649{col 79}{space 3}-1.017688
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .7539526{col 38}{space 2}  .151377{col 49}{space 1}    4.98{col 58}{space 3}0.000{col 66}{space 4} .4565814{col 79}{space 3} 1.051324
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .9566078{col 38}{space 2} .1335647{col 49}{space 1}    7.16{col 58}{space 3}0.000{col 66}{space 4} .6942278{col 79}{space 3} 1.218988
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 4.110284{col 38}{space 2} .0848762{col 49}{space 1}   48.43{col 58}{space 3}0.000{col 66}{space 4} 3.943549{col 79}{space 3} 4.277018
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. cem addvictimisis, treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}7
{txt}Number of matched strata: {res}4

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}362  168
{txt}Unmatched  {res}  0    7


{txt}Multivariate L1 distance: {res}1.780e-15

{txt}Univariate imbalance:

                     L1      mean       min       25%       50%       75%       max
addvictimisis  {res} 1.8e-15  -5.4e-16         0         0         0         0         0
{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis [pweight=cem_weights], robust
{txt}(sum of wgt is 530)

Linear regression                               Number of obs     = {res}       530
                                                {txt}F(5, 524)         =  {res}    25.78
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1004
                                                {txt}Root MSE          =    {res}  .5879

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0563515{col 38}{space 2} .1270386{col 49}{space 1}   -0.44{col 58}{space 3}0.658{col 66}{space 4} -.305919{col 79}{space 3}  .193216
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  -.11668{col 38}{space 2} .0931231{col 49}{space 1}   -1.25{col 58}{space 3}0.211{col 66}{space 4}-.2996205{col 79}{space 3} .0662604
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-.6756767{col 38}{space 2} .0758186{col 49}{space 1}   -8.91{col 58}{space 3}0.000{col 66}{space 4}-.8246224{col 79}{space 3}-.5267309
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .4299378{col 38}{space 2} .0903274{col 49}{space 1}    4.76{col 58}{space 3}0.000{col 66}{space 4} .2524896{col 79}{space 3}  .607386
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .0761525{col 38}{space 2}  .268227{col 49}{space 1}    0.28{col 58}{space 3}0.777{col 66}{space 4}-.4507799{col 79}{space 3} .6030848
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 4.148797{col 38}{space 2} .0864787{col 49}{space 1}   47.97{col 58}{space 3}0.000{col 66}{space 4}  3.97891{col 79}{space 3} 4.318685
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Victimization during Liberation (2017)
. 
. sum i.injuredlib-womenabusedlib

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}injuredlib {c |}
ISIS forces  {c |}{res}        537    .3910615    .4884431          0          1
{txt}Iraqi for..  {c |}{res}        537    .0018622    .0431532          0          1
{txt}Air strikes  {c |}{res}        537    .0819367     .274524          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
faminjured~b {c |}
ISIS forces  {c |}{res}        537    .3947858    .4892604          0          1
{txt}Air strikes  {c |}{res}        537    .0391061    .1940282          0          1
{txt}{space 12} {c |}
famkilledlib {c |}
ISIS forces  {c |}{res}        537    .3724395    .4839053          0          1
{txt}Air strikes  {c |}{res}        537    .0260708    .1594944          0          1
{txt}{space 12} {c |}
homedestro~b {c |}
ISIS forces  {c |}{res}        537    .2700186    .4443831          0          1
{txt}Iraqi for..  {c |}{res}        537    .0018622    .0431532          0          1
{txt}Air strikes  {c |}{res}        537    .1638734    .3705058          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
imprisoned~b {c |}
ISIS forces  {c |}{res}        537    .3407821    .4744142          0          1
{txt}Iraqi for..  {c |}{res}        537    .0074488    .0860645          0          1
{txt}{space 12} {c |}
{space 1}fleehomelib {c |}
ISIS forces  {c |}{res}        537    .5530726    .4976389          0          1
{txt}Air strikes  {c |}{res}        537    .0055866    .0746039          0          1
{txt}{space 12} {c |}
{space 3}lootedlib {c |}
ISIS forces  {c |}{res}        537    .4189944    .4938545          0          1
{txt}Iraqi for..  {c |}{res}        537    .0018622    .0431532          0          1
{txt}Air strikes  {c |}{res}        537    .0018622    .0431532          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
womenabuse~b {c |}
ISIS forces  {c |}{res}        537    .3556797    .4791651          0          1
{txt}Air strikes  {c |}{res}        537     .009311    .0961327          0          1
{txt}
{com}. 
. *Factor Analysis of ISIS Victimization During Liberation
. factor injuredlibisis-womenabusedlibisis
{txt}(obs=537)

Factor analysis/correlation{col 50}Number of obs    = {res}       537
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       4
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      26

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      6.00924      5.59640            0.9222       0.9222
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.41284      0.16791            0.0634       0.9856
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.24492      0.19273            0.0376       1.0232
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.05219      0.06940            0.0080       1.0312
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.01721      0.02276           -0.0026       1.0285
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.03997      0.00310           -0.0061       1.0224
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.04307      0.05982           -0.0066       1.0158
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.10289            .           -0.0158       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}28{txt}) ={res} 5571.01{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:injuredlib~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9177}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2816}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0025}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0919}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0702}}}{space 1}
{space 4}{space 0}{ralign 12:faminj~bisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9149}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3406}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0048}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0326}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0458}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledl~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9202}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1516}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0566}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0936}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1184}}}{space 1}
{space 4}{space 0}{ralign 12:homedestro~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7417}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1161}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1418}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1460}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3951}}}{space 1}
{space 4}{space 0}{ralign 12:impris~bisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9321}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1247}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0280}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0812}}}{space 1}
{space 4}{space 0}{ralign 12:fleehomeli~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7255}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0105}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3853}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0048}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3250}}}{space 1}
{space 4}{space 0}{ralign 12:lootedlibi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8343}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3424}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1704}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0080}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1576}}}{space 1}
{space 4}{space 0}{ralign 12:womena~bisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9176}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1735}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1689}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1084}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0876}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Figure 8. ISIS Victimization During Liberation Histogram
. histogram addvictimlibisis , discrete percent addlabel
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. *Factor Analysis of Victimization Due to Coalition Airstrikes
. factor injuredlibair- fleehomelibair
{txt}(obs=537)

Factor analysis/correlation{col 50}Number of obs    = {res}       537
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      10

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.54340      1.41749            1.1131       1.1131
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.12591      0.05098            0.0908       1.2039
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.07493      0.20132            0.0540       1.2579
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.12639      0.10482           -0.0911       1.1667
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.23121            .           -0.1667       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}10{txt}) ={res}  536.76{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:injuredlib~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5177}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0199}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1731}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7016}}}{space 1}
{space 4}{space 0}{ralign 12:faminjured~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7667}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0931}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0328}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4025}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledl~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8064}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0937}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0323}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3399}}}{space 1}
{space 4}{space 0}{ralign 12:homedestro~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1842}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1687}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1720}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9080}}}{space 1}
{space 4}{space 0}{ralign 12:fleehomeli~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0586}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2821}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1152}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9037}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Figure 9. Airstrike Victimization During Liberation Histogram
. histogram addvictimair , discrete percent addlabel
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. *Liberation Victimization and Treatment Effects (OLS regression)
. *To generate the victimization variable:
. *gen addvictimliball =  injuredlibisis + faminjuredlibisis + famkilledlibisis + homedestroyedlibisis + imprisonedlibisis + fleehomelibisis + lootedlibisis + womenabusedlibisis + injuredlibair + faminjuredlibair + famkilledlibair + homedestroyedlibair + fleehomelibair + lootedlibair + womenabusedlibair + injuredlibarmy + faminjuredlibarmy + famkilledlibarmy + homedestroyedlibarmy + imprisonedlibarmy + fleehomelibarmy + lootedlibarmy + womenabusedlibarmy
. 
. reg revpostalpha ib3.txt , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt i.injuredlib i.faminjuredlib i.famkilledlib i.homedestroyedlib i.imprisonedlib i.fleehomelib i.lootedlib i.womenabusedlib, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}{help j_robustsingular:F(17, 515) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.5546
                                                {txt}Root MSE          =    {res} .58076

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0891025{col 38}{space 2}  .093261{col 49}{space 1}   -0.96{col 58}{space 3}0.340{col 66}{space 4}-.2723212{col 79}{space 3} .0941162
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.1522972{col 38}{space 2} .1325901{col 49}{space 1}   -1.15{col 58}{space 3}0.251{col 66}{space 4}-.4127812{col 79}{space 3} .1081868
{txt}{space 24} {c |}
{space 14}injuredlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2} 1.559428{col 38}{space 2} .2751497{col 49}{space 1}    5.67{col 58}{space 3}0.000{col 66}{space 4} 1.018874{col 79}{space 3} 2.099982
{txt}{space 11}Iraqi forces  {c |}{col 26}{res}{space 2} 1.129058{col 38}{space 2} .3214418{col 49}{space 1}    3.51{col 58}{space 3}0.000{col 66}{space 4} .4975598{col 79}{space 3} 1.760557
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2}-.1237232{col 38}{space 2} .0989243{col 49}{space 1}   -1.25{col 58}{space 3}0.212{col 66}{space 4} -.318068{col 79}{space 3} .0706215
{txt}{space 24} {c |}
{space 11}faminjuredlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2}-.4368513{col 38}{space 2} .3071177{col 49}{space 1}   -1.42{col 58}{space 3}0.156{col 66}{space 4}-1.040209{col 79}{space 3} .1665062
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2} .3039096{col 38}{space 2} .2528479{col 49}{space 1}    1.20{col 58}{space 3}0.230{col 66}{space 4}-.1928305{col 79}{space 3} .8006497
{txt}{space 24} {c |}
{space 12}famkilledlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2} .2992417{col 38}{space 2} .1795006{col 49}{space 1}    1.67{col 58}{space 3}0.096{col 66}{space 4}-.0534018{col 79}{space 3} .6518852
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2} .9572188{col 38}{space 2} .3258356{col 49}{space 1}    2.94{col 58}{space 3}0.003{col 66}{space 4} .3170884{col 79}{space 3} 1.597349
{txt}{space 24} {c |}
{space 8}homedestroyedlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2}-.4572757{col 38}{space 2} .1224496{col 49}{space 1}   -3.73{col 58}{space 3}0.000{col 66}{space 4}-.6978378{col 79}{space 3}-.2167136
{txt}{space 11}Iraqi forces  {c |}{col 26}{res}{space 2}-.5793679{col 38}{space 2} .0909518{col 49}{space 1}   -6.37{col 58}{space 3}0.000{col 66}{space 4}-.7580501{col 79}{space 3}-.4006857
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2}-.3397247{col 38}{space 2}  .088189{col 49}{space 1}   -3.85{col 58}{space 3}0.000{col 66}{space 4}-.5129791{col 79}{space 3}-.1664702
{txt}{space 24} {c |}
{space 11}imprisonedlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2} .2847215{col 38}{space 2} .3121225{col 49}{space 1}    0.91{col 58}{space 3}0.362{col 66}{space 4}-.3284684{col 79}{space 3} .8979115
{txt}{space 11}Iraqi forces  {c |}{col 26}{res}{space 2} .6119874{col 38}{space 2} .3436697{col 49}{space 1}    1.78{col 58}{space 3}0.076{col 66}{space 4}-.0631795{col 79}{space 3} 1.287154
{txt}{space 24} {c |}
{space 13}fleehomelib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2} .0692557{col 38}{space 2} .1441261{col 49}{space 1}    0.48{col 58}{space 3}0.631{col 66}{space 4}-.2138916{col 79}{space 3}  .352403
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2} .2145815{col 38}{space 2}  .250413{col 49}{space 1}    0.86{col 58}{space 3}0.392{col 66}{space 4}-.2773752{col 79}{space 3} .7065382
{txt}{space 24} {c |}
{space 15}lootedlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2}-.7357211{col 38}{space 2} .1485443{col 49}{space 1}   -4.95{col 58}{space 3}0.000{col 66}{space 4}-1.027548{col 79}{space 3}-.4438938
{txt}{space 11}Iraqi forces  {c |}{col 26}{res}{space 2}-.8095211{col 38}{space 2} .1471439{col 49}{space 1}   -5.50{col 58}{space 3}0.000{col 66}{space 4}-1.098597{col 79}{space 3} -.520445
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2}-.3804968{col 38}{space 2} .3491928{col 49}{space 1}   -1.09{col 58}{space 3}0.276{col 66}{space 4}-1.066514{col 79}{space 3} .3055207
{txt}{space 24} {c |}
{space 10}womenabusedlib {c |}
{space 12}ISIS forces  {c |}{col 26}{res}{space 2} .4849224{col 38}{space 2} .3252574{col 49}{space 1}    1.49{col 58}{space 3}0.137{col 66}{space 4} -.154072{col 79}{space 3} 1.123917
{txt}{space 12}Air strikes  {c |}{col 26}{res}{space 2} .7120437{col 38}{space 2} .4839923{col 49}{space 1}    1.47{col 58}{space 3}0.142{col 66}{space 4}-.2387983{col 79}{space 3} 1.662886
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.079368{col 38}{space 2} .0909518{col 49}{space 1}   33.86{col 58}{space 3}0.000{col 66}{space 4} 2.900686{col 79}{space 3}  3.25805
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt addvictimliball, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   150.10
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3356
                                                {txt}Root MSE          =    {res} .69728

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} -.043391{col 38}{space 2} .0809694{col 49}{space 1}   -0.54{col 58}{space 3}0.592{col 66}{space 4}-.2024493{col 79}{space 3} .1156674
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.1856368{col 38}{space 2} .1403588{col 49}{space 1}   -1.32{col 58}{space 3}0.187{col 66}{space 4}-.4613612{col 79}{space 3} .0900875
{txt}{space 24} {c |}
{space 9}addvictimliball {c |}{col 26}{res}{space 2} .1602477{col 38}{space 2} .0159188{col 49}{space 1}   10.07{col 58}{space 3}0.000{col 66}{space 4} .1289764{col 79}{space 3} .1915191
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.930391{col 38}{space 2} .0739357{col 49}{space 1}   39.63{col 58}{space 3}0.000{col 66}{space 4}  2.78515{col 79}{space 3} 3.075632
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimliball, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    92.41
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3400
                                                {txt}Root MSE          =    {res} .69627

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0840169{col 38}{space 2} .1097689{col 49}{space 1}    0.77{col 58}{space 3}0.444{col 66}{space 4}-.1316178{col 79}{space 3} .2996516
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.1144328{col 38}{space 2} .4899235{col 49}{space 1}   -0.23{col 58}{space 3}0.815{col 66}{space 4}-1.076859{col 79}{space 3} .8479933
{txt}{space 24} {c |}
{space 9}addvictimliball {c |}{col 26}{res}{space 2} .2225811{col 38}{space 2} .0463043{col 49}{space 1}    4.81{col 58}{space 3}0.000{col 66}{space 4}  .131619{col 79}{space 3} .3135432
{txt}{space 24} {c |}
{space 3}txt#c.addvictimliball {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0805742{col 38}{space 2} .0475975{col 49}{space 1}   -1.69{col 58}{space 3}0.091{col 66}{space 4}-.1740767{col 79}{space 3} .0129283
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.0578386{col 38}{space 2} .0761314{col 49}{space 1}   -0.76{col 58}{space 3}0.448{col 66}{space 4}-.2073942{col 79}{space 3}  .091717
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 2.825181{col 38}{space 2} .0997263{col 49}{space 1}   28.33{col 58}{space 3}0.000{col 66}{space 4} 2.629275{col 79}{space 3} 3.021088
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Kolmogorov Smirnov Balance Tests on ISIS Victimization During Liberation
. 
. ksmirnov injuredlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.2529    0.000
{txt} Combined K-S:     {res}  0.2529    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.2584    0.000
{txt} Combined K-S:     {res}  0.2584    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.3448    0.000
{txt} Combined K-S:     {res}  0.3448    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.4857    0.000
{txt} Combined K-S:     {res}  0.4857    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0709    0.307
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0709    0.597

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.3216    0.000
{txt} Combined K-S:     {res}  0.3216    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedlibisis, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.5077    0.000
{txt} Combined K-S:     {res}  0.5077    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov injuredlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.4157    0.000
{txt} Combined K-S:     {res}  0.4157    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.4214    0.000
{txt} Combined K-S:     {res}  0.4214    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.3869    0.000
{txt} Combined K-S:     {res}  0.3869    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.3381    0.000
{txt} Combined K-S:     {res}  0.3381    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.6003    0.000
{txt} Combined K-S:     {res}  0.6003    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.2860    0.000
{txt} Combined K-S:     {res}  0.2860    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedlibisis, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.3529    0.000
{txt} Combined K-S:     {res}  0.3529    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov injuredlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.6829    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.6829    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.6943    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.6943    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.7445    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.7445    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov imprisonedlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.8338    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.8338    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.5528    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.5528    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.6167    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.6167    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov womenabusedlibisis, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.8710    0.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.8710    0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. *Figure 10. ISIS Victimization During Liberation Across Treatment Groups
. cibar addvictimlibisis, over1(txt2)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 10 bar isis victimization liberation by txt.grec" file with the command graph play " Figure 10 bar isis victimization liberation by txt.grec"
. 
. *Treatment Effects With Coarsened Exact Matching on ISIS Victimization Pre-Liberation (OLS Regression)
. 
. cem addvictimlibisis, treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}9
{txt}Number of matched strata: {res}8

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}362  171
{txt}Unmatched  {res}  0    4


{txt}Multivariate L1 distance: {res}3.084e-15

{txt}Univariate imbalance:

                       L1     mean      min      25%      50%      75%      max
addvictimlibisis  {res}3.1e-15  5.8e-14        0        0        0        0        0
{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimisis ib3.txt##c.addvictimlibisis [pweight=cem_weights], robust
{txt}(sum of wgt is 533)

Linear regression                               Number of obs     = {res}       533
                                                {txt}F(8, 524)         =  {res}    48.58
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3363
                                                {txt}Root MSE          =    {res} .43067

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2001061{col 38}{space 2} .6199445{col 49}{space 1}   -0.32{col 58}{space 3}0.747{col 66}{space 4}-1.417988{col 79}{space 3} 1.017776
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.4715188{col 38}{space 2} .2994635{col 49}{space 1}   -1.57{col 58}{space 3}0.116{col 66}{space 4}-1.059815{col 79}{space 3} .1167777
{txt}{space 24} {c |}
{space 11}addvictimisis {c |}{col 26}{res}{space 2}-1.105004{col 38}{space 2} .0835383{col 49}{space 1}  -13.23{col 58}{space 3}0.000{col 66}{space 4}-1.269115{col 79}{space 3}-.9408924
{txt}{space 24} {c |}
{space 5}txt#c.addvictimisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}  .307944{col 38}{space 2} .3751605{col 49}{space 1}    0.82{col 58}{space 3}0.412{col 66}{space 4}-.4290594{col 79}{space 3} 1.044947
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .8637166{col 38}{space 2} .1304848{col 49}{space 1}    6.62{col 58}{space 3}0.000{col 66}{space 4} .6073789{col 79}{space 3} 1.120054
{txt}{space 24} {c |}
{space 8}addvictimlibisis {c |}{col 26}{res}{space 2}-.0310693{col 38}{space 2} .0333661{col 49}{space 1}   -0.93{col 58}{space 3}0.352{col 66}{space 4} -.096617{col 79}{space 3} .0344784
{txt}{space 24} {c |}
{space 2}txt#c.addvictimlibisis {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0218038{col 38}{space 2} .0847034{col 49}{space 1}    0.26{col 58}{space 3}0.797{col 66}{space 4}-.1445961{col 79}{space 3} .1882038
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .0399826{col 38}{space 2} .0477985{col 49}{space 1}    0.84{col 58}{space 3}0.403{col 66}{space 4}-.0539176{col 79}{space 3} .1338829
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  4.41127{col 38}{space 2} .1539881{col 49}{space 1}   28.65{col 58}{space 3}0.000{col 66}{space 4}  4.10876{col 79}{space 3} 4.713779
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Kolmogorov Smirnov Balance Tests on Airstrike Victimization During Liberation
. 
. ksmirnov injuredlibair, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0441    0.633
{txt} Combined K-S:     {res}  0.0441    0.976

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibair, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0236    0.878
{txt} Combined K-S:     {res}  0.0236    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibair, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0299    0.810
{txt} Combined K-S:     {res}  0.0299    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibair, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0088    0.982
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0088    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibair, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0627    0.398
{txt} Combined K-S:     {res}  0.0627    0.746

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov injuredlibair, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0532    0.500
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0532    0.879

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibair, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0603    0.410
{txt} Combined K-S:     {res}  0.0603    0.764

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibair, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0402    0.673
{txt} Combined K-S:     {res}  0.0402    0.989

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibair, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0086    0.982
{txt} Combined K-S:     {res}  0.0086    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibair, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0977    0.096
{txt} Combined K-S:     {res}  0.0977    0.192

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. ksmirnov injuredlibair, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0113    0.970
{txt} Combined K-S:     {res}  0.0113    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov faminjuredlibair, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0861    0.174
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0861    0.346

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov famkilledlibair, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0715    0.299
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0715    0.582

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov fleehomelibair, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0002    1.000
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0002    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov homedestroyedlibair, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.1638    0.002
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.1638    0.004

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. *Figure 11. Airstrike Victimization by Treatment Group
. cibar addvictimair, over1(txt2)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 11 bar airstrike victimization by txt.grec" file with the command graph play " Figure 11 bar airstrike victimization by txt.grec"
. 
. *Treatment Effects With Coarsened Exact Matching on Airstrike Victimization During Liberation (OLS Regression)
. 
. cem addvictimair, treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}5
{txt}Number of matched strata: {res}2

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}362  158
{txt}Unmatched  {res}  0   17


{txt}Multivariate L1 distance: {res}1.971e-15

{txt}Univariate imbalance:

                   L1     mean      min      25%      50%      75%      max
addvictimair  {res}2.0e-15  1.7e-16        0        0        0        0        0
{txt}
{com}. reg revpostalpha ib3.txt##c.addvictimair [pweight=cem_weights], robust
{txt}(sum of wgt is 520)

Linear regression                               Number of obs     = {res}       520
                                                {txt}F(5, 514)         =  {res}    43.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2344
                                                {txt}Root MSE          =    {res} .75086

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2373311{col 38}{space 2}  .102121{col 49}{space 1}   -2.32{col 58}{space 3}0.021{col 66}{space 4} -.437957{col 79}{space 3}-.0367051
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5833333{col 38}{space 2} .1031151{col 49}{space 1}    5.66{col 58}{space 3}0.000{col 66}{space 4} .3807545{col 79}{space 3} .7859121
{txt}{space 24} {c |}
{space 12}addvictimair {c |}{col 26}{res}{space 2}-.6680743{col 38}{space 2} .1359025{col 49}{space 1}   -4.92{col 58}{space 3}0.000{col 66}{space 4}-.9350671{col 79}{space 3}-.4010816
{txt}{space 24} {c |}
{space 6}txt#c.addvictimair {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .5555578{col 38}{space 2} .1814888{col 49}{space 1}    3.06{col 58}{space 3}0.002{col 66}{space 4} .1990068{col 79}{space 3} .9121088
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  .740991{col 38}{space 2} .1779861{col 49}{space 1}    4.16{col 58}{space 3}0.000{col 66}{space 4} .3913212{col 79}{space 3} 1.090661
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.34375{col 38}{space 2} .0870743{col 49}{space 1}   38.40{col 58}{space 3}0.000{col 66}{space 4} 3.172685{col 79}{space 3} 3.514815
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Kolmogorov Smirnov Balance Tests on Crossfire Victimization During Liberation
. 
. ksmirnov crossfire, by(powersharetxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0614    0.413
{txt} Combined K-S:     {res}  0.0614    0.768

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov crossfire, by(hashdpgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0000    1.000
{txt} 1:                {res} -0.0417    0.653
{txt} Combined K-S:     {res}  0.0417    0.983

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. ksmirnov crossfire, by(localpolicepgtxt)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.1043    0.077
{txt} 1:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.1043    0.153

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 537 observations.

{com}. 
. *Figure 12. Crossfire Victimization by Treatment Group
. cibar crossfire, over1(txt2)
{res}{txt}
{com}. *Note additional formatting requires the "Figure 12 crossfire victimization by txt.grec" file with the command graph play " Figure 12 crossfire victimization by txt.grec"
. 
. *Treatment Effects With Coarsened Exact Matching on Crossfire Victimization During Liberation (OLS Regression)
. 
. cem crossfire, treatment(localpolicepgtxt)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}2
{txt}Number of matched strata: {res}2

           {txt}  0    1
      All  {res}362  175
{txt}  Matched  {res}362  175
{txt}Unmatched  {res}  0    0


{txt}Multivariate L1 distance: {res}1.665e-15

{txt}Univariate imbalance:

                L1     mean      min      25%      50%      75%      max
crossfire  {res}1.7e-15  2.2e-16        0        0        0        0        0
{txt}
{com}. reg revpostalpha ib3.txt##c.crossfire [pweight=cem_weights], robust
{txt}(sum of wgt is 537)

Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    52.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2757
                                                {txt}Root MSE          =    {res} .72978

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2414072{col 38}{space 2}  .101471{col 49}{space 1}   -2.38{col 58}{space 3}0.018{col 66}{space 4}-.4407409{col 79}{space 3}-.0420734
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5792572{col 38}{space 2} .1024709{col 49}{space 1}    5.65{col 58}{space 3}0.000{col 66}{space 4} .3779591{col 79}{space 3} .7805554
{txt}{space 24} {c |}
{space 15}crossfire {c |}{col 26}{res}{space 2}-.7263975{col 38}{space 2} .1295237{col 49}{space 1}   -5.61{col 58}{space 3}0.000{col 66}{space 4}-.9808393{col 79}{space 3}-.4719557
{txt}{space 24} {c |}
{space 9}txt#c.crossfire {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}  .613881{col 38}{space 2} .1767476{col 49}{space 1}    3.47{col 58}{space 3}0.001{col 66}{space 4} .2666707{col 79}{space 3} .9610914
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .8220415{col 38}{space 2} .1610549{col 49}{space 1}    5.10{col 58}{space 3}0.000{col 66}{space 4} .5056585{col 79}{space 3} 1.138424
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.347826{col 38}{space 2}  .086317{col 49}{space 1}   38.79{col 58}{space 3}0.000{col 66}{space 4} 3.178261{col 79}{space 3} 3.517391
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *ISIS Victimization and Post-Traumatic Stress
. 
. *Relationship between Victimization and Fear/Anxiety (OLS regression).
. *To generate victimization variable: 
. *gen addvictimlibarmy = injuredlibarmy + faminjuredlibarmy + famkilledlibarmy + homedestroyedlibarmy + imprisonedlibarmy + fleehomelibarmy + lootedlibarmy + womenabusedlibarmy
. 
. reg afraid addvictimlibisis addvictimair addvictimlibarmy, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}     3.30
                                                {txt}Prob > F          = {res}    0.0201
                                                {txt}R-squared         = {res}    0.0127
                                                {txt}Root MSE          =    {res} .95494

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          afraid{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addvictimlibisis {c |}{col 18}{res}{space 2}-.0178313{col 30}{space 2}  .011709{col 41}{space 1}   -1.52{col 50}{space 3}0.128{col 58}{space 4}-.0408327{col 71}{space 3} .0051701
{txt}{space 4}addvictimair {c |}{col 18}{res}{space 2}-.1326352{col 30}{space 2} .0555193{col 41}{space 1}   -2.39{col 50}{space 3}0.017{col 58}{space 4}-.2416986{col 71}{space 3}-.0235718
{txt}addvictimlibarmy {c |}{col 18}{res}{space 2}-.0214047{col 30}{space 2} .4062909{col 41}{space 1}   -0.05{col 50}{space 3}0.958{col 58}{space 4}-.8195326{col 71}{space 3} .7767231
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 2.041623{col 30}{space 2} .0633571{col 41}{space 1}   32.22{col 50}{space 3}0.000{col 58}{space 4} 1.917162{col 71}{space 3} 2.166083
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg worried addvictimlibisis addvictimair addvictimlibarmy, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}    11.88
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0428
                                                {txt}Root MSE          =    {res} 1.0698

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         worried{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addvictimlibisis {c |}{col 18}{res}{space 2} .0210429{col 30}{space 2}  .012952{col 41}{space 1}    1.62{col 50}{space 3}0.105{col 58}{space 4}-.0044003{col 71}{space 3} .0464861
{txt}{space 4}addvictimair {c |}{col 18}{res}{space 2}-.3228868{col 30}{space 2} .0580694{col 41}{space 1}   -5.56{col 50}{space 3}0.000{col 58}{space 4}-.4369597{col 71}{space 3}-.2088139
{txt}addvictimlibarmy {c |}{col 18}{res}{space 2} .5952126{col 30}{space 2} .3946051{col 41}{space 1}    1.51{col 50}{space 3}0.132{col 58}{space 4}-.1799594{col 71}{space 3} 1.370385
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}  2.48553{col 30}{space 2} .0746679{col 41}{space 1}   33.29{col 50}{space 3}0.000{col 58}{space 4} 2.338851{col 71}{space 3}  2.63221
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg afraid addvictimisis, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(1, 535)         =  {res}    10.83
                                                {txt}Prob > F          = {res}    0.0011
                                                {txt}R-squared         = {res}    0.0381
                                                {txt}Root MSE          =    {res} .94081

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}       afraid{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addvictimisis {c |}{col 15}{res}{space 2} .1905026{col 27}{space 2} .0578849{col 38}{space 1}    3.29{col 47}{space 3}0.001{col 55}{space 4} .0767931{col 68}{space 3} .3042121
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.788043{col 27}{space 2} .0509258{col 38}{space 1}   35.11{col 47}{space 3}0.000{col 55}{space 4} 1.688003{col 68}{space 3} 1.888082
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg worried addvictimisis, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(1, 535)         =  {res}     3.21
                                                {txt}Prob > F          = {res}    0.0736
                                                {txt}R-squared         = {res}    0.0096
                                                {txt}Root MSE          =    {res} 1.0861

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}      worried{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addvictimisis {c |}{col 15}{res}{space 2}   .10871{col 27}{space 2} .0606522{col 38}{space 1}    1.79{col 47}{space 3}0.074{col 55}{space 4}-.0104357{col 68}{space 3} .2278557
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.367165{col 27}{space 2}  .056898{col 38}{space 1}   41.60{col 47}{space 3}0.000{col 55}{space 4} 2.255394{col 68}{space 3} 2.478936
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Demographic Correlates of Victimization (OLS Regression)
. 
. reg addvictimisis gender age education unemployed income i.religion i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(9, 527)         =  {res}    18.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1982
                                                {txt}Root MSE          =    {res} .88733

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}addvictimi~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2}-.2720485{col 26}{space 2}  .091039{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.4508923{col 67}{space 3}-.0932047
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0313881{col 26}{space 2} .0051412{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .0212883{col 67}{space 3} .0414879
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.1286789{col 26}{space 2} .0705645{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-.2673012{col 67}{space 3} .0099434
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2}-.5044612{col 26}{space 2} .0831502{col 37}{space 1}   -6.07{col 46}{space 3}0.000{col 54}{space 4}-.6678077{col 67}{space 3}-.3411148
{txt}{space 6}income {c |}{col 14}{res}{space 2} -.216927{col 26}{space 2} .0454422{col 37}{space 1}   -4.77{col 46}{space 3}0.000{col 54}{space 4}-.3061971{col 67}{space 3}-.1276568
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2} 1.033837{col 26}{space 2} .6404138{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.2242403{col 67}{space 3} 2.291914
{txt}{space 2}Christian  {c |}{col 14}{res}{space 2} .5521232{col 26}{space 2} .2380901{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4}  .084401{col 67}{space 3} 1.019846
{txt}{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{col 14}{res}{space 2}-.2782638{col 26}{space 2} .1997485{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.6706649{col 67}{space 3} .1141373
{txt}{space 4}Turkmen  {c |}{col 14}{res}{space 2} 1.628407{col 26}{space 2} 1.443672{col 37}{space 1}    1.13{col 46}{space 3}0.260{col 54}{space 4}-1.207652{col 67}{space 3} 4.464465
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.049077{col 26}{space 2} .2721593{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .5144267{col 67}{space 3} 1.583727
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg addvictimlibisis gender age education unemployed income i.religion i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(9, 527)         =  {res}    34.93
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1534
                                                {txt}Root MSE          =    {res} 3.1332

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}addvictiml~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2} .7827843{col 26}{space 2}  .336102{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4}   .12252{col 67}{space 3} 1.443048
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0939351{col 26}{space 2} .0146123{col 37}{space 1}   -6.43{col 46}{space 3}0.000{col 54}{space 4}-.1226406{col 67}{space 3}-.0652296
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0345997{col 26}{space 2} .2142098{col 37}{space 1}    0.16{col 46}{space 3}0.872{col 54}{space 4}-.3862103{col 67}{space 3} .4554097
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} 1.100835{col 26}{space 2}  .348799{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .4156274{col 67}{space 3} 1.786042
{txt}{space 6}income {c |}{col 14}{res}{space 2} .8975888{col 26}{space 2}  .169764{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .5640914{col 67}{space 3} 1.231086
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2}-1.511312{col 26}{space 2} 1.322855{col 37}{space 1}   -1.14{col 46}{space 3}0.254{col 54}{space 4}-4.110029{col 67}{space 3} 1.087404
{txt}{space 2}Christian  {c |}{col 14}{res}{space 2}-3.498181{col 26}{space 2} .8664518{col 37}{space 1}   -4.04{col 46}{space 3}0.000{col 54}{space 4}-5.200305{col 67}{space 3}-1.796058
{txt}{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{col 14}{res}{space 2}  .305024{col 26}{space 2} .8695478{col 37}{space 1}    0.35{col 46}{space 3}0.726{col 54}{space 4}-1.403182{col 67}{space 3}  2.01323
{txt}{space 4}Turkmen  {c |}{col 14}{res}{space 2}  .327933{col 26}{space 2} .4129822{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.4833605{col 67}{space 3} 1.139226
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.076634{col 26}{space 2} .8067593{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} 1.491775{col 67}{space 3} 4.661493
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg addvictimlibarmy gender age education unemployed income i.religion i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(9, 527)         =  {res}     0.75
                                                {txt}Prob > F          = {res}    0.6591
                                                {txt}R-squared         = {res}    0.0718
                                                {txt}Root MSE          =    {res} .12526

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}addvictiml~y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2}    .0099{col 26}{space 2}  .011241{col 37}{space 1}    0.88{col 46}{space 3}0.379{col 54}{space 4}-.0121827{col 67}{space 3} .0319827
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .000588{col 26}{space 2} .0008294{col 37}{space 1}    0.71{col 46}{space 3}0.479{col 54}{space 4}-.0010412{col 67}{space 3} .0022173
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0123215{col 26}{space 2} .0087286{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.0048256{col 67}{space 3} .0294687
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} .0085004{col 26}{space 2} .0134504{col 37}{space 1}    0.63{col 46}{space 3}0.528{col 54}{space 4}-.0179226{col 67}{space 3} .0349234
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0032925{col 26}{space 2} .0069619{col 37}{space 1}   -0.47{col 46}{space 3}0.636{col 54}{space 4}-.0169689{col 67}{space 3} .0103839
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2}-.0125233{col 26}{space 2} .0096333{col 37}{space 1}   -1.30{col 46}{space 3}0.194{col 54}{space 4}-.0314477{col 67}{space 3} .0064011
{txt}{space 2}Christian  {c |}{col 14}{res}{space 2} -.156767{col 26}{space 2} .1491457{col 37}{space 1}   -1.05{col 46}{space 3}0.294{col 54}{space 4}  -.44976{col 67}{space 3}  .136226
{txt}{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{col 14}{res}{space 2} .1464567{col 26}{space 2} .1477121{col 37}{space 1}    0.99{col 46}{space 3}0.322{col 54}{space 4}-.1437201{col 67}{space 3} .4366335
{txt}{space 4}Turkmen  {c |}{col 14}{res}{space 2}   .32523{col 26}{space 2} .2681618{col 37}{space 1}    1.21{col 46}{space 3}0.226{col 54}{space 4}-.2015673{col 67}{space 3} .8520273
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0471669{col 26}{space 2} .0236418{col 37}{space 1}   -2.00{col 46}{space 3}0.047{col 54}{space 4}-.0936107{col 67}{space 3}-.0007231
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg addvictimair gender age education unemployed income i.religion i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(9, 527)         =  {res}     8.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0995
                                                {txt}Root MSE          =    {res} .64593

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}addvictimair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2} .0241773{col 26}{space 2} .0819497{col 37}{space 1}    0.30{col 46}{space 3}0.768{col 54}{space 4}-.1368109{col 67}{space 3} .1851655
{txt}{space 9}age {c |}{col 14}{res}{space 2} -.008132{col 26}{space 2} .0035102{col 37}{space 1}   -2.32{col 46}{space 3}0.021{col 54}{space 4}-.0150277{col 67}{space 3}-.0012363
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1000304{col 26}{space 2} .0604191{col 37}{space 1}    1.66{col 46}{space 3}0.098{col 54}{space 4}-.0186615{col 67}{space 3} .2187222
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} .0547477{col 26}{space 2} .0807592{col 37}{space 1}    0.68{col 46}{space 3}0.498{col 54}{space 4}-.1039017{col 67}{space 3} .2133972
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0654847{col 26}{space 2} .0383189{col 37}{space 1}   -1.71{col 46}{space 3}0.088{col 54}{space 4}-.1407612{col 67}{space 3} .0097917
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2} 1.004564{col 26}{space 2} .7581414{col 37}{space 1}    1.33{col 46}{space 3}0.186{col 54}{space 4} -.484786{col 67}{space 3} 2.493915
{txt}{space 2}Christian  {c |}{col 14}{res}{space 2}-1.177307{col 26}{space 2} .3623662{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4}-1.889166{col 67}{space 3}-.4654471
{txt}{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{col 14}{res}{space 2}  .919993{col 26}{space 2} .3617073{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .2094278{col 67}{space 3} 1.630558
{txt}{space 4}Turkmen  {c |}{col 14}{res}{space 2} .9773233{col 26}{space 2} .7184315{col 37}{space 1}    1.36{col 46}{space 3}0.174{col 54}{space 4} -.434018{col 67}{space 3} 2.388664
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .3458468{col 26}{space 2} .1715662{col 37}{space 1}    2.02{col 46}{space 3}0.044{col 54}{space 4} .0088091{col 67}{space 3} .6828844
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. logit crossfire gender age education unemployed income i.religion i.ethnicity, robust

{txt}note: 3.religion != 0 predicts failure perfectly
      3.religion dropped and 12 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-294.95098}  
Iteration 1:{space 3}log pseudolikelihood = {res:-282.79294}  
Iteration 2:{space 3}log pseudolikelihood = {res:-282.42318}  
Iteration 3:{space 3}log pseudolikelihood = {res:-282.42193}  
Iteration 4:{space 3}log pseudolikelihood = {res:-282.42193}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       525
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}     19.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0110
{txt}Log pseudolikelihood = {res}-282.42193{txt}{col 49}Pseudo R2{col 67}= {res}    0.0425

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   crossfire{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2}-.5457584{col 26}{space 2} .2624256{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-1.060103{col 67}{space 3}-.0314137
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0348383{col 26}{space 2} .0139418{col 37}{space 1}   -2.50{col 46}{space 3}0.012{col 54}{space 4}-.0621638{col 67}{space 3}-.0075128
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .090777{col 26}{space 2} .2014391{col 37}{space 1}    0.45{col 46}{space 3}0.652{col 54}{space 4}-.3040364{col 67}{space 3} .4855903
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} .1644521{col 26}{space 2} .2484365{col 37}{space 1}    0.66{col 46}{space 3}0.508{col 54}{space 4}-.3224745{col 67}{space 3} .6513786
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.1437562{col 26}{space 2} .1225919{col 37}{space 1}   -1.17{col 46}{space 3}0.241{col 54}{space 4}-.3840319{col 67}{space 3} .0965196
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2} 1.608545{col 26}{space 2} .8186648{col 37}{space 1}    1.96{col 46}{space 3}0.049{col 54}{space 4}  .003991{col 67}{space 3} 3.213098
{txt}{space 2}Christian  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Kurd  {c |}{col 14}{res}{space 2} 1.767828{col 26}{space 2} .6672559{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .4600307{col 67}{space 3} 3.075626
{txt}{space 4}Turkmen  {c |}{col 14}{res}{space 2} 1.842734{col 26}{space 2} 1.421433{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.9432241{col 67}{space 3} 4.628691
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0052189{col 26}{space 2} .7005726{col 37}{space 1}   -0.01{col 46}{space 3}0.994{col 54}{space 4}-1.378316{col 67}{space 3} 1.367878
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Sectarian and Local/Non-Local Social Distance
. 
. reg revpostalpha ib3.txt, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revclosemosul revclosebaghdad, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(4, 532)         =  {res}    68.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3374
                                                {txt}Root MSE          =    {res} .69695

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0383983{col 38}{space 2} .0778554{col 49}{space 1}   -0.49{col 58}{space 3}0.622{col 66}{space 4}  -.19134{col 79}{space 3} .1145434
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .2528842{col 38}{space 2} .0972159{col 49}{space 1}    2.60{col 58}{space 3}0.010{col 66}{space 4}   .06191{col 79}{space 3} .4438584
{txt}{space 24} {c |}
{space 11}revclosemosul {c |}{col 26}{res}{space 2} .0285171{col 38}{space 2} .0416753{col 49}{space 1}    0.68{col 58}{space 3}0.494{col 66}{space 4}-.0533513{col 79}{space 3} .1103854
{txt}{space 9}revclosebaghdad {c |}{col 26}{res}{space 2} .3018885{col 38}{space 2} .0325055{col 49}{space 1}    9.29{col 58}{space 3}0.000{col 66}{space 4} .2380337{col 79}{space 3} .3657433
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.472262{col 38}{space 2} .1576862{col 49}{space 1}   15.68{col 58}{space 3}0.000{col 66}{space 4} 2.162498{col 79}{space 3} 2.782026
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revclosebaghdad revcloseshia, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(4, 532)         =  {res}    72.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3372
                                                {txt}Root MSE          =    {res} .69707

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0512549{col 38}{space 2} .0744562{col 49}{space 1}   -0.69{col 58}{space 3}0.492{col 66}{space 4}-.1975192{col 79}{space 3} .0950094
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .2497046{col 38}{space 2} .1002959{col 49}{space 1}    2.49{col 58}{space 3}0.013{col 66}{space 4} .0526799{col 79}{space 3} .4467293
{txt}{space 24} {c |}
{space 9}revclosebaghdad {c |}{col 26}{res}{space 2} .3025103{col 38}{space 2} .0312661{col 49}{space 1}    9.68{col 58}{space 3}0.000{col 66}{space 4} .2410901{col 79}{space 3} .3639305
{txt}{space 12}revcloseshia {c |}{col 26}{res}{space 2} .0160674{col 38}{space 2}  .046586{col 49}{space 1}    0.34{col 58}{space 3}0.730{col 66}{space 4}-.0754476{col 79}{space 3} .1075825
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.557076{col 38}{space 2} .0921895{col 49}{space 1}   27.74{col 58}{space 3}0.000{col 66}{space 4} 2.375976{col 79}{space 3} 2.738176
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Cognizance of Baghdad/Shia majority status in Mosul
. 
. reg revpostalpha ib3.txt, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revbaghdadisshia revmosulissunni , robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(4, 532)         =  {res}    49.46
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2172
                                                {txt}Root MSE          =    {res} .75753

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1793474{col 38}{space 2} .0900758{col 49}{space 1}   -1.99{col 58}{space 3}0.047{col 66}{space 4}-.3562953{col 79}{space 3}-.0023996
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .6681052{col 38}{space 2} .0983432{col 49}{space 1}    6.79{col 58}{space 3}0.000{col 66}{space 4} .4749164{col 79}{space 3} .8612939
{txt}{space 24} {c |}
{space 8}revbaghdadisshia {c |}{col 26}{res}{space 2} .1924092{col 38}{space 2} .0782162{col 49}{space 1}    2.46{col 58}{space 3}0.014{col 66}{space 4} .0387586{col 79}{space 3} .3460597
{txt}{space 9}revmosulissunni {c |}{col 26}{res}{space 2}-.1462916{col 38}{space 2} .0684059{col 49}{space 1}   -2.14{col 58}{space 3}0.033{col 66}{space 4}-.2806705{col 79}{space 3}-.0119128
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.196954{col 38}{space 2} .1509018{col 49}{space 1}   21.19{col 58}{space 3}0.000{col 66}{space 4} 2.900518{col 79}{space 3}  3.49339
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Baghdad/Shia Intergroup Contact and Travel between Mosul and Baghdad
. 
. reg revpostalpha ib3.txt, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt revcontactbaghdad traveltobaghdad, robust

{txt}Linear regression                               Number of obs     = {res}       516
                                                {txt}F(4, 511)         =  {res}    69.58
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2961
                                                {txt}Root MSE          =    {res} .73066

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2607826{col 38}{space 2} .0857598{col 49}{space 1}   -3.04{col 58}{space 3}0.002{col 66}{space 4}-.4292678{col 79}{space 3}-.0922975
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7605871{col 38}{space 2} .0837556{col 49}{space 1}    9.08{col 58}{space 3}0.000{col 66}{space 4} .5960394{col 79}{space 3} .9251348
{txt}{space 24} {c |}
{space 7}revcontactbaghdad {c |}{col 26}{res}{space 2} -.212476{col 38}{space 2} .0515203{col 49}{space 1}   -4.12{col 58}{space 3}0.000{col 66}{space 4}-.3136938{col 79}{space 3}-.1112583
{txt}{space 9}traveltobaghdad {c |}{col 26}{res}{space 2} -.160301{col 38}{space 2} .0443572{col 49}{space 1}   -3.61{col 58}{space 3}0.000{col 66}{space 4} -.247446{col 79}{space 3} -.073156
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 4.066337{col 38}{space 2} .1557866{col 49}{space 1}   26.10{col 58}{space 3}0.000{col 66}{space 4} 3.760276{col 79}{space 3} 4.372398
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Emotional Mediators of Treatment Effects
. 
. *Emotions across Treatment Groups (Ordered Probit Regression)
. oprobit afraid ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-654.74645}  
Iteration 1:{space 3}log pseudolikelihood = {res:-650.34607}  
Iteration 2:{space 3}log pseudolikelihood = {res:-650.34567}  
Iteration 3:{space 3}log pseudolikelihood = {res:-650.34567}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}      9.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0080
{txt}Log pseudolikelihood = {res}-650.34567{txt}{col 49}Pseudo R2{col 67}= {res}    0.0067

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  afraid{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.3198395{col 38}{space 2} .1086825{col 49}{space 1}   -2.94{col 58}{space 3}0.003{col 66}{space 4}-.5328533{col 79}{space 3}-.1068257
{txt}Local police power grab  {c |}{col 26}{res}{space 2} -.269257{col 38}{space 2}  .120107{col 49}{space 1}   -2.24{col 58}{space 3}0.025{col 66}{space 4}-.5046624{col 79}{space 3}-.0338517
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-.5828426{col 38}{space 2} .0833608{col 66}{space 4}-.7462269{col 79}{space 3}-.4194583
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} .6791707{col 38}{space 2}  .084602{col 66}{space 4} .5133538{col 79}{space 3} .8449875
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 1.263732{col 38}{space 2} .1014182{col 66}{space 4} 1.064956{col 79}{space 3} 1.462508
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 1.669077{col 38}{space 2} .1286373{col 66}{space 4} 1.416953{col 79}{space 3} 1.921202
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit angry ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-579.11679}  
Iteration 1:{space 3}log pseudolikelihood = {res:-550.35765}  
Iteration 2:{space 3}log pseudolikelihood = {res:-550.33401}  
Iteration 3:{space 3}log pseudolikelihood = {res:-550.33401}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}     63.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-550.33401{txt}{col 49}Pseudo R2{col 67}= {res}    0.0497

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                   angry{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}  .109461{col 38}{space 2} .1276143{col 49}{space 1}    0.86{col 58}{space 3}0.391{col 66}{space 4}-.1406584{col 79}{space 3} .3595804
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .8502797{col 38}{space 2} .1154501{col 49}{space 1}    7.36{col 58}{space 3}0.000{col 66}{space 4} .6240017{col 79}{space 3} 1.076558
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2} .4507059{col 38}{space 2} .0877015{col 66}{space 4} .2788141{col 79}{space 3} .6225977
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} 1.245765{col 38}{space 2} .0889776{col 66}{space 4} 1.071372{col 79}{space 3} 1.420158
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 2.473443{col 38}{space 2} .1460144{col 66}{space 4}  2.18726{col 79}{space 3} 2.759626
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 2.862481{col 38}{space 2} .1868121{col 66}{space 4} 2.496336{col 79}{space 3} 3.228626
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit sad ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-723.17303}  
Iteration 1:{space 3}log pseudolikelihood = {res:-681.04982}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -681.017}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -681.017}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    103.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}  -681.017{txt}{col 49}Pseudo R2{col 67}= {res}    0.0583

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                     sad{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1078049{col 38}{space 2} .1315674{col 49}{space 1}    0.82{col 58}{space 3}0.413{col 66}{space 4}-.1500624{col 79}{space 3} .3656722
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .9759739{col 38}{space 2} .1079807{col 49}{space 1}    9.04{col 58}{space 3}0.000{col 66}{space 4} .7643356{col 79}{space 3} 1.187612
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2} .0426242{col 38}{space 2} .0923415{col 66}{space 4}-.1383619{col 79}{space 3} .2236103
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} .6964023{col 38}{space 2} .0962868{col 66}{space 4} .5076837{col 79}{space 3}  .885121
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 1.638343{col 38}{space 2} .1084321{col 66}{space 4}  1.42582{col 79}{space 3} 1.850866
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 2.777063{col 38}{space 2} .1810034{col 66}{space 4} 2.422303{col 79}{space 3} 3.131823
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit worried ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-764.68293}  
Iteration 1:{space 3}log pseudolikelihood = {res: -762.3306}  
Iteration 2:{space 3}log pseudolikelihood = {res:-762.33053}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}      4.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1124
{txt}Log pseudolikelihood = {res}-762.33053{txt}{col 49}Pseudo R2{col 67}= {res}    0.0031

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                 worried{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1670562{col 38}{space 2}  .117401{col 49}{space 1}   -1.42{col 58}{space 3}0.155{col 66}{space 4}-.3971579{col 79}{space 3} .0630455
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .0676009{col 38}{space 2} .1041107{col 49}{space 1}    0.65{col 58}{space 3}0.516{col 66}{space 4}-.1364524{col 79}{space 3} .2716541
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-.8922003{col 38}{space 2} .0850249{col 66}{space 4}-1.058846{col 79}{space 3}-.7255545
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2}  .194755{col 38}{space 2} .0816128{col 66}{space 4} .0347968{col 79}{space 3} .3547131
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} .7653522{col 38}{space 2} .0874533{col 66}{space 4} .5939468{col 79}{space 3} .9367575
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 1.846527{col 38}{space 2} .1383241{col 66}{space 4} 1.575416{col 79}{space 3} 2.117637
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit happy ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-613.90247}  
Iteration 1:{space 3}log pseudolikelihood = {res:-508.76876}  
Iteration 2:{space 3}log pseudolikelihood = {res:-507.94453}  
Iteration 3:{space 3}log pseudolikelihood = {res:-507.94395}  
Iteration 4:{space 3}log pseudolikelihood = {res:-507.94395}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    199.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-507.94395{txt}{col 49}Pseudo R2{col 67}= {res}    0.1726

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                   happy{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.6412187{col 38}{space 2} .1482369{col 49}{space 1}   -4.33{col 58}{space 3}0.000{col 66}{space 4}-.9317577{col 79}{space 3}-.3506797
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.212213{col 38}{space 2} .1307653{col 49}{space 1}    9.27{col 58}{space 3}0.000{col 66}{space 4} .9559181{col 79}{space 3} 1.468509
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2} .2055843{col 38}{space 2} .1003836{col 66}{space 4}  .008836{col 79}{space 3} .4023325
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} .8227064{col 38}{space 2} .1106328{col 66}{space 4} .6058701{col 79}{space 3} 1.039543
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 2.386904{col 38}{space 2} .1688406{col 66}{space 4} 2.055982{col 79}{space 3} 2.717825
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 3.236418{col 38}{space 2} .2731721{col 66}{space 4}  2.70101{col 79}{space 3} 3.771825
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit satisfied ib3.txt, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-676.45969}  
Iteration 1:{space 3}log pseudolikelihood = {res: -596.7885}  
Iteration 2:{space 3}log pseudolikelihood = {res:-596.52072}  
Iteration 3:{space 3}log pseudolikelihood = {res:-596.52062}  
Iteration 4:{space 3}log pseudolikelihood = {res:-596.52062}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    175.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-596.52062{txt}{col 49}Pseudo R2{col 67}= {res}    0.1182

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}               satisfied{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.3860292{col 38}{space 2} .1350651{col 49}{space 1}   -2.86{col 58}{space 3}0.004{col 66}{space 4} -.650752{col 79}{space 3}-.1213064
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.107093{col 38}{space 2} .1285999{col 49}{space 1}    8.61{col 58}{space 3}0.000{col 66}{space 4}  .855042{col 79}{space 3} 1.359145
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2} .1930333{col 38}{space 2} .1019813{col 66}{space 4}-.0068463{col 79}{space 3} .3929129
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} .9064736{col 38}{space 2} .1146674{col 66}{space 4} .6817297{col 79}{space 3} 1.131218
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 1.914746{col 38}{space 2} .1394379{col 66}{space 4} 1.641452{col 79}{space 3} 2.188039
{txt}{space 19}/cut4 {c |}{col 26}{res}{space 2} 2.383666{col 38}{space 2} .1419851{col 66}{space 4} 2.105381{col 79}{space 3} 2.661952
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Emotional Affect and Security
. reg revpostalpha ib3.txt, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(2, 534)         =  {res}    97.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2058
                                                {txt}Root MSE          =    {res}  .7616

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1188565{col 38}{space 2} .0887641{col 49}{space 1}   -1.34{col 58}{space 3}0.181{col 66}{space 4}-.2932261{col 79}{space 3} .0555131
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7562758{col 38}{space 2} .0873293{col 49}{space 1}    8.66{col 58}{space 3}0.000{col 66}{space 4} .5847247{col 79}{space 3} .9278269
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.200867{col 38}{space 2} .0747582{col 49}{space 1}   42.82{col 58}{space 3}0.000{col 66}{space 4} 3.054011{col 79}{space 3} 3.347723
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt afraid angry sad worried happy satisfied, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(8, 528)         =  {res}   102.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6000
                                                {txt}Root MSE          =    {res} .54355

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0399822{col 38}{space 2} .0574208{col 49}{space 1}   -0.70{col 58}{space 3}0.487{col 66}{space 4}-.1527835{col 79}{space 3} .0728191
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .0474856{col 38}{space 2} .0792955{col 49}{space 1}    0.60{col 58}{space 3}0.550{col 66}{space 4}-.1082877{col 79}{space 3}  .203259
{txt}{space 24} {c |}
{space 18}afraid {c |}{col 26}{res}{space 2}-.3127451{col 38}{space 2} .0372808{col 49}{space 1}   -8.39{col 58}{space 3}0.000{col 66}{space 4} -.385982{col 79}{space 3}-.2395082
{txt}{space 19}angry {c |}{col 26}{res}{space 2} .2205715{col 38}{space 2} .0552022{col 49}{space 1}    4.00{col 58}{space 3}0.000{col 66}{space 4} .1121286{col 79}{space 3} .3290145
{txt}{space 21}sad {c |}{col 26}{res}{space 2}-.0183568{col 38}{space 2} .0512113{col 49}{space 1}   -0.36{col 58}{space 3}0.720{col 66}{space 4}-.1189598{col 79}{space 3} .0822462
{txt}{space 17}worried {c |}{col 26}{res}{space 2} .0923838{col 38}{space 2} .0349357{col 49}{space 1}    2.64{col 58}{space 3}0.008{col 66}{space 4} .0237537{col 79}{space 3} .1610139
{txt}{space 19}happy {c |}{col 26}{res}{space 2} .3713218{col 38}{space 2} .0456946{col 49}{space 1}    8.13{col 58}{space 3}0.000{col 66}{space 4} .2815562{col 79}{space 3} .4610873
{txt}{space 15}satisfied {c |}{col 26}{res}{space 2} .1381853{col 38}{space 2} .0382249{col 49}{space 1}    3.62{col 58}{space 3}0.000{col 66}{space 4} .0630937{col 79}{space 3} .2132769
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.507641{col 38}{space 2} .1056318{col 49}{space 1}   23.74{col 58}{space 3}0.000{col 66}{space 4} 2.300131{col 79}{space 3} 2.715151
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revpostalpha ib3.txt afraid, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   117.98
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2854
                                                {txt}Root MSE          =    {res}  .7231

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} -.185164{col 38}{space 2}  .085771{col 49}{space 1}   -2.16{col 58}{space 3}0.031{col 66}{space 4}-.3536547{col 79}{space 3}-.0166733
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7083201{col 38}{space 2} .0844229{col 49}{space 1}    8.39{col 58}{space 3}0.000{col 66}{space 4} .5424777{col 79}{space 3} .8741625
{txt}{space 24} {c |}
{space 18}afraid {c |}{col 26}{res}{space 2} -.252805{col 38}{space 2} .0346551{col 49}{space 1}   -7.29{col 58}{space 3}0.000{col 66}{space 4}-.3208824{col 79}{space 3}-.1847277
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.731319{col 38}{space 2} .1039857{col 49}{space 1}   35.88{col 58}{space 3}0.000{col 66}{space 4} 3.527047{col 79}{space 3} 3.935591
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt angry, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}    58.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2623
                                                {txt}Root MSE          =    {res} .73472

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1453757{col 38}{space 2} .0803847{col 49}{space 1}   -1.81{col 58}{space 3}0.071{col 66}{space 4}-.3032854{col 79}{space 3}  .012534
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5948067{col 38}{space 2}  .086181{col 49}{space 1}    6.90{col 58}{space 3}0.000{col 66}{space 4} .4255107{col 79}{space 3} .7641027
{txt}{space 24} {c |}
{space 19}angry {c |}{col 26}{res}{space 2} .2475303{col 38}{space 2} .0502785{col 49}{space 1}    4.92{col 58}{space 3}0.000{col 66}{space 4}  .148762{col 79}{space 3} .3462987
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.848887{col 38}{space 2} .0956603{col 49}{space 1}   29.78{col 58}{space 3}0.000{col 66}{space 4}  2.66097{col 79}{space 3} 3.036805
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt sad, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}    68.02
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3285
                                                {txt}Root MSE          =    {res} .70096

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1447537{col 38}{space 2}  .075123{col 49}{space 1}   -1.93{col 58}{space 3}0.055{col 66}{space 4}-.2923272{col 79}{space 3} .0028199
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .4729527{col 38}{space 2} .0861713{col 49}{space 1}    5.49{col 58}{space 3}0.000{col 66}{space 4} .3036757{col 79}{space 3} .6422297
{txt}{space 24} {c |}
{space 21}sad {c |}{col 26}{res}{space 2} .2997382{col 38}{space 2} .0358587{col 49}{space 1}    8.36{col 58}{space 3}0.000{col 66}{space 4} .2292965{col 79}{space 3} .3701799
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.663764{col 38}{space 2} .0860191{col 49}{space 1}   30.97{col 58}{space 3}0.000{col 66}{space 4} 2.494786{col 79}{space 3} 2.832742
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt worried, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}    64.58
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2123
                                                {txt}Root MSE          =    {res} .75923

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} -.113405{col 38}{space 2} .0876279{col 49}{space 1}   -1.29{col 58}{space 3}0.196{col 66}{space 4}-.2855434{col 79}{space 3} .0587333
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .7515815{col 38}{space 2} .0880834{col 49}{space 1}    8.53{col 58}{space 3}0.000{col 66}{space 4} .5785484{col 79}{space 3} .9246147
{txt}{space 24} {c |}
{space 17}worried {c |}{col 26}{res}{space 2} .0628289{col 38}{space 2} .0340216{col 49}{space 1}    1.85{col 58}{space 3}0.065{col 66}{space 4} -.004004{col 79}{space 3} .1296618
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.046155{col 38}{space 2} .1019035{col 49}{space 1}   29.89{col 58}{space 3}0.000{col 66}{space 4} 2.845974{col 79}{space 3} 3.246337
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt happy, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   145.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4593
                                                {txt}Root MSE          =    {res} .62898

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0795613{col 38}{space 2} .0664532{col 49}{space 1}    1.20{col 58}{space 3}0.232{col 66}{space 4} -.050981{col 79}{space 3} .2101036
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .2015502{col 38}{space 2}  .088133{col 49}{space 1}    2.29{col 58}{space 3}0.023{col 66}{space 4} .0284196{col 79}{space 3} .3746807
{txt}{space 24} {c |}
{space 19}happy {c |}{col 26}{res}{space 2} .5493366{col 38}{space 2} .0409875{col 49}{space 1}   13.40{col 58}{space 3}0.000{col 66}{space 4} .4688197{col 79}{space 3} .6298534
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.299066{col 38}{space 2}  .082239{col 49}{space 1}   27.96{col 58}{space 3}0.000{col 66}{space 4} 2.137514{col 79}{space 3} 2.460618
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt satisfied, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(3, 533)         =  {res}   116.92
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4361
                                                {txt}Root MSE          =    {res} .64235

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0114549{col 38}{space 2} .0659375{col 49}{space 1}    0.17{col 58}{space 3}0.862{col 66}{space 4}-.1180744{col 79}{space 3} .1409842
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .3058673{col 38}{space 2} .0865525{col 49}{space 1}    3.53{col 58}{space 3}0.000{col 66}{space 4} .1358414{col 79}{space 3} .4758931
{txt}{space 24} {c |}
{space 15}satisfied {c |}{col 26}{res}{space 2} .4412127{col 38}{space 2} .0373996{col 49}{space 1}   11.80{col 58}{space 3}0.000{col 66}{space 4} .3677439{col 79}{space 3} .5146815
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.461262{col 38}{space 2} .0833516{col 49}{space 1}   29.53{col 58}{space 3}0.000{col 66}{space 4} 2.297524{col 79}{space 3}    2.625
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revpostalpha ib3.txt##c.afraid, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    72.32
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2862
                                                {txt}Root MSE          =    {res} .72405

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2843827{col 38}{space 2}  .170936{col 49}{space 1}   -1.66{col 58}{space 3}0.097{col 66}{space 4}-.6201765{col 79}{space 3} .0514112
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .5933884{col 38}{space 2} .1801704{col 49}{space 1}    3.29{col 58}{space 3}0.001{col 66}{space 4} .2394542{col 79}{space 3} .9473226
{txt}{space 24} {c |}
{space 18}afraid {c |}{col 26}{res}{space 2}-.2878437{col 38}{space 2} .0519712{col 49}{space 1}   -5.54{col 58}{space 3}0.000{col 66}{space 4} -.389938{col 79}{space 3}-.1857494
{txt}{space 24} {c |}
{space 12}txt#c.afraid {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .0490357{col 38}{space 2} .0730691{col 49}{space 1}    0.67{col 58}{space 3}0.502{col 66}{space 4}-.0945043{col 79}{space 3} .1925757
{txt}Local police power grab  {c |}{col 26}{res}{space 2} .0567362{col 38}{space 2} .0868123{col 49}{space 1}    0.65{col 58}{space 3}0.514{col 66}{space 4}-.1138015{col 79}{space 3} .2272738
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.80484{col 38}{space 2} .1356886{col 49}{space 1}   28.04{col 58}{space 3}0.000{col 66}{space 4} 3.538287{col 79}{space 3} 4.071392
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.angry, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}   124.52
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4367
                                                {txt}Root MSE          =    {res} .64322

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .2693892{col 38}{space 2} .1727878{col 49}{space 1}    1.56{col 58}{space 3}0.120{col 66}{space 4}-.0700423{col 79}{space 3} .6088208
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 2.380077{col 38}{space 2} .1890821{col 49}{space 1}   12.59{col 58}{space 3}0.000{col 66}{space 4} 2.008636{col 79}{space 3} 2.751518
{txt}{space 24} {c |}
{space 19}angry {c |}{col 26}{res}{space 2} .8472756{col 38}{space 2} .1116794{col 49}{space 1}    7.59{col 58}{space 3}0.000{col 66}{space 4} .6278879{col 79}{space 3} 1.066663
{txt}{space 24} {c |}
{space 13}txt#c.angry {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.3132683{col 38}{space 2} .1166286{col 49}{space 1}   -2.69{col 58}{space 3}0.007{col 66}{space 4}-.5423783{col 79}{space 3}-.0841583
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-1.049275{col 38}{space 2} .1249668{col 49}{space 1}   -8.40{col 58}{space 3}0.000{col 66}{space 4}-1.294765{col 79}{space 3}-.8037851
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 1.996071{col 38}{space 2} .1581507{col 49}{space 1}   12.62{col 58}{space 3}0.000{col 66}{space 4} 1.685393{col 79}{space 3} 2.306748
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.sad, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}   143.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4433
                                                {txt}Root MSE          =    {res} .63944

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .2210223{col 38}{space 2} .1275039{col 49}{space 1}    1.73{col 58}{space 3}0.084{col 66}{space 4}-.0294516{col 79}{space 3} .4714963
{txt}Local police power grab  {c |}{col 26}{res}{space 2}  2.34314{col 38}{space 2} .2581983{col 49}{space 1}    9.07{col 58}{space 3}0.000{col 66}{space 4} 1.835925{col 79}{space 3} 2.850355
{txt}{space 24} {c |}
{space 21}sad {c |}{col 26}{res}{space 2} .5777334{col 38}{space 2} .0608659{col 49}{space 1}    9.49{col 58}{space 3}0.000{col 66}{space 4} .4581658{col 79}{space 3}  .697301
{txt}{space 24} {c |}
{space 15}txt#c.sad {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2075245{col 38}{space 2} .0639022{col 49}{space 1}   -3.25{col 58}{space 3}0.001{col 66}{space 4}-.3330566{col 79}{space 3}-.0819923
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.7792644{col 38}{space 2} .1062223{col 49}{space 1}   -7.34{col 58}{space 3}0.000{col 66}{space 4} -.987932{col 79}{space 3}-.5705969
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 2.165622{col 38}{space 2} .1153395{col 49}{space 1}   18.78{col 58}{space 3}0.000{col 66}{space 4} 1.939045{col 79}{space 3}   2.3922
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.worried, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    60.03
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2427
                                                {txt}Root MSE          =    {res} .74581

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} -.495018{col 38}{space 2} .1921035{col 49}{space 1}   -2.58{col 58}{space 3}0.010{col 66}{space 4} -.872394{col 79}{space 3} -.117642
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.203644{col 38}{space 2} .2432392{col 49}{space 1}    4.95{col 58}{space 3}0.000{col 66}{space 4} .7258146{col 79}{space 3} 1.681473
{txt}{space 24} {c |}
{space 17}worried {c |}{col 26}{res}{space 2} .0384996{col 38}{space 2} .0716212{col 49}{space 1}    0.54{col 58}{space 3}0.591{col 66}{space 4}-.1021961{col 79}{space 3} .1791952
{txt}{space 24} {c |}
{space 11}txt#c.worried {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1597458{col 38}{space 2} .0794189{col 49}{space 1}    2.01{col 58}{space 3}0.045{col 66}{space 4}  .003732{col 79}{space 3} .3157596
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.1774612{col 38}{space 2} .1005747{col 49}{space 1}   -1.76{col 58}{space 3}0.078{col 66}{space 4}-.3750344{col 79}{space 3} .0201119
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.106065{col 38}{space 2}  .172321{col 49}{space 1}   18.02{col 58}{space 3}0.000{col 66}{space 4}  2.76755{col 79}{space 3} 3.444579
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.happy, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}   121.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5167
                                                {txt}Root MSE          =    {res} .59578

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}   .44824{col 38}{space 2} .1370503{col 49}{space 1}    3.27{col 58}{space 3}0.001{col 66}{space 4} .1790127{col 79}{space 3} .7174674
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.545758{col 38}{space 2} .2713535{col 49}{space 1}    5.70{col 58}{space 3}0.000{col 66}{space 4}   1.0127{col 79}{space 3} 2.078816
{txt}{space 24} {c |}
{space 19}happy {c |}{col 26}{res}{space 2} .7977708{col 38}{space 2} .0701637{col 49}{space 1}   11.37{col 58}{space 3}0.000{col 66}{space 4} .6599383{col 79}{space 3} .9356032
{txt}{space 24} {c |}
{space 13}txt#c.happy {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.2178542{col 38}{space 2} .0839909{col 49}{space 1}   -2.59{col 58}{space 3}0.010{col 66}{space 4}-.3828493{col 79}{space 3} -.052859
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.6015922{col 38}{space 2} .1097408{col 49}{space 1}   -5.48{col 58}{space 3}0.000{col 66}{space 4}-.8171716{col 79}{space 3}-.3860128
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 1.891232{col 38}{space 2} .1124625{col 49}{space 1}   16.82{col 58}{space 3}0.000{col 66}{space 4} 1.670306{col 79}{space 3} 2.112158
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.satisfied, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}   144.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5622
                                                {txt}Root MSE          =    {res} .56705

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            revpostalpha{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .1134354{col 38}{space 2} .1479115{col 49}{space 1}    0.77{col 58}{space 3}0.443{col 66}{space 4}-.1771281{col 79}{space 3} .4039989
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.795453{col 38}{space 2} .1971754{col 49}{space 1}    9.11{col 58}{space 3}0.000{col 66}{space 4} 1.408113{col 79}{space 3} 2.182792
{txt}{space 24} {c |}
{space 15}satisfied {c |}{col 26}{res}{space 2} .7318484{col 38}{space 2} .0502772{col 49}{space 1}   14.56{col 58}{space 3}0.000{col 66}{space 4} .6330817{col 79}{space 3} .8306151
{txt}{space 24} {c |}
{space 9}txt#c.satisfied {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.0116889{col 38}{space 2} .0996544{col 49}{space 1}   -0.12{col 58}{space 3}0.907{col 66}{space 4}-.2074541{col 79}{space 3} .1840763
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.6622855{col 38}{space 2} .0733347{col 49}{space 1}   -9.03{col 58}{space 3}0.000{col 66}{space 4}-.8063472{col 79}{space 3}-.5182237
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 1.974069{col 38}{space 2} .0947262{col 49}{space 1}   20.84{col 58}{space 3}0.000{col 66}{space 4} 1.787985{col 79}{space 3} 2.160153
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Material and Social-Psychological Motivations
. 
. reg revposteconomy ib3.txt##c.income, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    81.70
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3390
                                                {txt}Root MSE          =    {res} 1.1038

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revposteconomy{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2} .6102469{col 38}{space 2}  .374293{col 49}{space 1}    1.63{col 58}{space 3}0.104{col 66}{space 4}-.1250298{col 79}{space 3} 1.345524
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 2.330888{col 38}{space 2} .3862681{col 49}{space 1}    6.03{col 58}{space 3}0.000{col 66}{space 4} 1.572087{col 79}{space 3} 3.089689
{txt}{space 24} {c |}
{space 18}income {c |}{col 26}{res}{space 2} .3769296{col 38}{space 2} .1284358{col 49}{space 1}    2.93{col 58}{space 3}0.003{col 66}{space 4}  .124625{col 79}{space 3} .6292342
{txt}{space 24} {c |}
{space 12}txt#c.income {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.4030165{col 38}{space 2} .1455507{col 49}{space 1}   -2.77{col 58}{space 3}0.006{col 66}{space 4}-.6889424{col 79}{space 3}-.1170907
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-.3716653{col 38}{space 2} .1440678{col 49}{space 1}   -2.58{col 58}{space 3}0.010{col 66}{space 4}-.6546781{col 79}{space 3}-.0886524
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 1.608387{col 38}{space 2} .3319331{col 49}{space 1}    4.85{col 58}{space 3}0.000{col 66}{space 4} .9563235{col 79}{space 3}  2.26045
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revposteconomy ib3.txt##c.unemployed, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(5, 531)         =  {res}    86.80
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3772
                                                {txt}Root MSE          =    {res} 1.0715

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revposteconomy{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}txt {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-.1939323{col 38}{space 2} .1396016{col 49}{space 1}   -1.39{col 58}{space 3}0.165{col 66}{space 4}-.4681715{col 79}{space 3} .0803069
{txt}Local police power grab  {c |}{col 26}{res}{space 2} 1.631927{col 38}{space 2} .1395509{col 49}{space 1}   11.69{col 58}{space 3}0.000{col 66}{space 4} 1.357788{col 79}{space 3} 1.906067
{txt}{space 24} {c |}
{space 14}unemployed {c |}{col 26}{res}{space 2} 1.316877{col 38}{space 2} .2553593{col 49}{space 1}    5.16{col 58}{space 3}0.000{col 66}{space 4} .8152387{col 79}{space 3} 1.818516
{txt}{space 24} {c |}
{space 8}txt#c.unemployed {c |}
{space 7}Hashd power grab  {c |}{col 26}{res}{space 2}-1.032563{col 38}{space 2}  .320133{col 49}{space 1}   -3.23{col 58}{space 3}0.001{col 66}{space 4}-1.661446{col 79}{space 3}-.4036809
{txt}Local police power grab  {c |}{col 26}{res}{space 2}-1.247312{col 38}{space 2} .2874422{col 49}{space 1}   -4.34{col 58}{space 3}0.000{col 66}{space 4}-1.811975{col 79}{space 3}-.6826486
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 2.298507{col 38}{space 2} .1177214{col 49}{space 1}   19.52{col 58}{space 3}0.000{col 66}{space 4} 2.067251{col 79}{space 3} 2.529764
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revpostalpha ib3.txt##c.revimportanceofethnicity i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}F(7, 529)         =  {res}    44.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2312
                                                {txt}Root MSE          =    {res} .75287

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                  revpostalpha{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}txt {c |}
{space 13}Hashd power grab  {c |}{col 32}{res}{space 2}-.1509441{col 44}{space 2}  .396422{col 55}{space 1}   -0.38{col 64}{space 3}0.704{col 72}{space 4}-.9296986{col 85}{space 3} .6278105
{txt}{space 6}Local police power grab  {c |}{col 32}{res}{space 2} 1.487404{col 44}{space 2} .4104329{col 55}{space 1}    3.62{col 64}{space 3}0.000{col 72}{space 4} .6811253{col 85}{space 3} 2.293682
{txt}{space 30} {c |}
{space 6}revimportanceofethnicity {c |}{col 32}{res}{space 2} .1155699{col 44}{space 2} .0735525{col 55}{space 1}    1.57{col 64}{space 3}0.117{col 72}{space 4}-.0289211{col 85}{space 3} .2600608
{txt}{space 30} {c |}
txt#c.revimportanceofethnicity {c |}
{space 13}Hashd power grab  {c |}{col 32}{res}{space 2} .0116832{col 44}{space 2} .1164366{col 55}{space 1}    0.10{col 64}{space 3}0.920{col 72}{space 4}-.2170518{col 85}{space 3} .2404181
{txt}{space 6}Local police power grab  {c |}{col 32}{res}{space 2}-.2016095{col 44}{space 2} .1158661{col 55}{space 1}   -1.74{col 64}{space 3}0.082{col 72}{space 4}-.4292237{col 85}{space 3} .0260047
{txt}{space 30} {c |}
{space 21}ethnicity {c |}
{space 25}Kurd  {c |}{col 32}{res}{space 2} .6012424{col 44}{space 2} .0710889{col 55}{space 1}    8.46{col 64}{space 3}0.000{col 72}{space 4} .4615913{col 85}{space 3} .7408936
{txt}{space 22}Turkmen  {c |}{col 32}{res}{space 2} .3357497{col 44}{space 2} .4373547{col 55}{space 1}    0.77{col 64}{space 3}0.443{col 72}{space 4}-.5234154{col 85}{space 3} 1.194915
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.742325{col 44}{space 2} .2442746{col 55}{space 1}   11.23{col 64}{space 3}0.000{col 72}{space 4} 2.262458{col 85}{space 3} 3.222193
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpostalpha ib3.txt##c.revimportanceofreligion i.religion, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}{help j_robustsingular:F(6, 529) }        =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2312
                                                {txt}Root MSE          =    {res} .75285

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                 revpostalpha{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      t{col 63}   P>|t|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}txt {c |}
{space 12}Hashd power grab  {c |}{col 31}{res}{space 2}-.6530084{col 43}{space 2} .4839072{col 54}{space 1}   -1.35{col 63}{space 3}0.178{col 71}{space 4}-1.603624{col 84}{space 3} .2976072
{txt}{space 5}Local police power grab  {c |}{col 31}{res}{space 2} .9372842{col 43}{space 2} .5453295{col 54}{space 1}    1.72{col 63}{space 3}0.086{col 71}{space 4}-.1339929{col 84}{space 3} 2.008561
{txt}{space 29} {c |}
{space 6}revimportanceofreligion {c |}{col 31}{res}{space 2} .0339605{col 43}{space 2} .1253726{col 54}{space 1}    0.27{col 63}{space 3}0.787{col 71}{space 4}-.2123288{col 84}{space 3} .2802497
{txt}{space 29} {c |}
txt#c.revimportanceofreligion {c |}
{space 12}Hashd power grab  {c |}{col 31}{res}{space 2} .1485931{col 43}{space 2} .1358577{col 54}{space 1}    1.09{col 63}{space 3}0.275{col 71}{space 4}-.1182937{col 84}{space 3}   .41548
{txt}{space 5}Local police power grab  {c |}{col 31}{res}{space 2}-.0482509{col 43}{space 2} .1482312{col 54}{space 1}   -0.33{col 63}{space 3}0.745{col 71}{space 4}-.3394449{col 84}{space 3} .2429432
{txt}{space 29} {c |}
{space 21}religion {c |}
{space 24}Shia  {c |}{col 31}{res}{space 2} .3902771{col 43}{space 2} .1251118{col 54}{space 1}    3.12{col 63}{space 3}0.002{col 71}{space 4} .1445001{col 84}{space 3}  .636054
{txt}{space 19}Christian  {c |}{col 31}{res}{space 2} .6042354{col 43}{space 2} .0729222{col 54}{space 1}    8.29{col 63}{space 3}0.000{col 71}{space 4} .4609827{col 84}{space 3}  .747488
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2} 3.068559{col 43}{space 2} .4592683{col 54}{space 1}    6.68{col 63}{space 3}0.000{col 71}{space 4} 2.166345{col 84}{space 3} 3.970772
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Correlates of Power-sharing Support
. 
. *Figure 13. Support for Powersharing
. 
. histogram powershare, discrete percent addlabel
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. *Note additional formatting requires the "Figure 12 crossfire victimization by txt.grec" file with the command graph play " Figure 12 crossfire victimization by txt.grec"
. 
. *Correlates of Power-sharing Support
. reg revpowershare addvictimisis gender age education ib8.typeofwork income i.religion i.ethnicity, robust

{txt}Linear regression                               Number of obs     = {res}       537
                                                {txt}{help j_robustsingular:F(16, 518) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2652
                                                {txt}Root MSE          =    {res} .88387

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            revpowershare{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}addvictimisis {c |}{col 27}{res}{space 2}-.3263371{col 39}{space 2} .0595389{col 50}{space 1}   -5.48{col 59}{space 3}0.000{col 67}{space 4}-.4433045{col 80}{space 3}-.2093698
{txt}{space 19}gender {c |}{col 27}{res}{space 2} .1776938{col 39}{space 2} .1057527{col 50}{space 1}    1.68{col 59}{space 3}0.094{col 67}{space 4}-.0300632{col 80}{space 3} .3854507
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0049384{col 39}{space 2}  .006705{col 50}{space 1}   -0.74{col 59}{space 3}0.462{col 67}{space 4}-.0181106{col 80}{space 3} .0082339
{txt}{space 16}education {c |}{col 27}{res}{space 2} .1874862{col 39}{space 2}  .080294{col 50}{space 1}    2.33{col 59}{space 3}0.020{col 67}{space 4} .0297442{col 80}{space 3} .3452281
{txt}{space 25} {c |}
{space 15}typeofwork {c |}
{space 9}employer/manage  {c |}{col 27}{res}{space 2}-.5535129{col 39}{space 2} .1724762{col 50}{space 1}   -3.21{col 59}{space 3}0.001{col 67}{space 4}-.8923518{col 80}{space 3}-.2146741
{txt}{space 5}professional worker  {c |}{col 27}{res}{space 2}-.3445884{col 39}{space 2} .1666277{col 50}{space 1}   -2.07{col 59}{space 3}0.039{col 67}{space 4}-.6719376{col 80}{space 3}-.0172393
{txt}office worker, secretary  {c |}{col 27}{res}{space 2} .0883748{col 39}{space 2} .1373369{col 50}{space 1}    0.64{col 59}{space 3}0.520{col 67}{space 4}-.1814309{col 80}{space 3} .3581805
{txt}{space 11}manual worker  {c |}{col 27}{res}{space 2}-.0226559{col 39}{space 2} .1330622{col 50}{space 1}   -0.17{col 59}{space 3}0.865{col 67}{space 4}-.2840638{col 80}{space 3} .2387521
{txt}{space 5}agricultural worker  {c |}{col 27}{res}{space 2} .0047825{col 39}{space 2} .1113733{col 50}{space 1}    0.04{col 59}{space 3}0.966{col 67}{space 4}-.2140164{col 80}{space 3} .2235814
{txt}{space 2}armed forces, security  {c |}{col 27}{res}{space 2}-.4809556{col 39}{space 2}  .431577{col 50}{space 1}   -1.11{col 59}{space 3}0.266{col 67}{space 4}-1.328812{col 80}{space 3} .3669007
{txt}{space 17}student  {c |}{col 27}{res}{space 2} .1368095{col 39}{space 2}  .122365{col 50}{space 1}    1.12{col 59}{space 3}0.264{col 67}{space 4}-.1035831{col 80}{space 3} .3772021
{txt}{space 15}pensioner  {c |}{col 27}{res}{space 2}  2.05783{col 39}{space 2} .2977542{col 50}{space 1}    6.91{col 59}{space 3}0.000{col 67}{space 4} 1.472875{col 80}{space 3} 2.642784
{txt}{space 19}other  {c |}{col 27}{res}{space 2} .0169386{col 39}{space 2}  .440852{col 50}{space 1}    0.04{col 59}{space 3}0.969{col 67}{space 4} -.849139{col 80}{space 3} .8830163
{txt}{space 25} {c |}
{space 19}income {c |}{col 27}{res}{space 2} .0620865{col 39}{space 2} .0511827{col 50}{space 1}    1.21{col 59}{space 3}0.226{col 67}{space 4}-.0384646{col 80}{space 3} .1626376
{txt}{space 25} {c |}
{space 17}religion {c |}
{space 20}Shia  {c |}{col 27}{res}{space 2} 1.675141{col 39}{space 2} .2535933{col 50}{space 1}    6.61{col 59}{space 3}0.000{col 67}{space 4} 1.176943{col 80}{space 3} 2.173339
{txt}{space 15}Christian  {c |}{col 27}{res}{space 2} .0583786{col 39}{space 2} .2656565{col 50}{space 1}    0.22{col 59}{space 3}0.826{col 67}{space 4}-.4635179{col 80}{space 3} .5802751
{txt}{space 25} {c |}
{space 16}ethnicity {c |}
{space 20}Kurd  {c |}{col 27}{res}{space 2} .6849585{col 39}{space 2} .2595641{col 50}{space 1}    2.64{col 59}{space 3}0.009{col 67}{space 4} .1750307{col 80}{space 3} 1.194886
{txt}{space 17}Turkmen  {c |}{col 27}{res}{space 2} 1.247255{col 39}{space 2} .5701611{col 50}{space 1}    2.19{col 59}{space 3}0.029{col 67}{space 4} .1271422{col 80}{space 3} 2.367367
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 1.982589{col 39}{space 2} .3503846{col 50}{space 1}    5.66{col 59}{space 3}0.000{col 67}{space 4} 1.294239{col 80}{space 3} 2.670938
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. oprobit revpowershare addvictimisis gender age education ib8.typeofwork income i.religion i.ethnicity, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-723.42764}  
Iteration 1:{space 3}log pseudolikelihood = {res:-642.85161}  
Iteration 2:{space 3}log pseudolikelihood = {res:-641.76108}  
Iteration 3:{space 3}log pseudolikelihood = {res:-641.63491}  
Iteration 4:{space 3}log pseudolikelihood = {res:-641.62215}  
Iteration 5:{space 3}log pseudolikelihood = {res:-641.61996}  
Iteration 6:{space 3}log pseudolikelihood = {res:-641.61969}  
Iteration 7:{space 3}log pseudolikelihood = {res:-641.61963}  
Iteration 8:{space 3}log pseudolikelihood = {res:-641.61962}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}       537
{txt}{col 49}Wald chi2({res}18{txt}){col 67}= {res}   1340.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-641.61962{txt}{col 49}Pseudo R2{col 67}= {res}    0.1131

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            revpowershare{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      z{col 59}   P>|z|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}addvictimisis {c |}{col 27}{res}{space 2}-.3953432{col 39}{space 2} .0797253{col 50}{space 1}   -4.96{col 59}{space 3}0.000{col 67}{space 4}-.5516019{col 80}{space 3}-.2390845
{txt}{space 19}gender {c |}{col 27}{res}{space 2} .1929968{col 39}{space 2}   .12981{col 50}{space 1}    1.49{col 59}{space 3}0.137{col 67}{space 4}-.0614262{col 80}{space 3} .4474197
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0050328{col 39}{space 2} .0086166{col 50}{space 1}   -0.58{col 59}{space 3}0.559{col 67}{space 4} -.021921{col 80}{space 3} .0118554
{txt}{space 16}education {c |}{col 27}{res}{space 2} .2535295{col 39}{space 2} .0986468{col 50}{space 1}    2.57{col 59}{space 3}0.010{col 67}{space 4} .0601852{col 80}{space 3} .4468737
{txt}{space 25} {c |}
{space 15}typeofwork {c |}
{space 9}employer/manage  {c |}{col 27}{res}{space 2}-.6914788{col 39}{space 2} .2223118{col 50}{space 1}   -3.11{col 59}{space 3}0.002{col 67}{space 4}-1.127202{col 80}{space 3}-.2557557
{txt}{space 5}professional worker  {c |}{col 27}{res}{space 2}-.4986433{col 39}{space 2} .2087527{col 50}{space 1}   -2.39{col 59}{space 3}0.017{col 67}{space 4}-.9077911{col 80}{space 3}-.0894954
{txt}office worker, secretary  {c |}{col 27}{res}{space 2} .0687724{col 39}{space 2} .1717434{col 50}{space 1}    0.40{col 59}{space 3}0.689{col 67}{space 4}-.2678385{col 80}{space 3} .4053833
{txt}{space 11}manual worker  {c |}{col 27}{res}{space 2}-.0446312{col 39}{space 2} .1627238{col 50}{space 1}   -0.27{col 59}{space 3}0.784{col 67}{space 4} -.363564{col 80}{space 3} .2743016
{txt}{space 5}agricultural worker  {c |}{col 27}{res}{space 2}-.0122578{col 39}{space 2}  .137683{col 50}{space 1}   -0.09{col 59}{space 3}0.929{col 67}{space 4}-.2821115{col 80}{space 3} .2575958
{txt}{space 2}armed forces, security  {c |}{col 27}{res}{space 2}-.6796754{col 39}{space 2} .5857916{col 50}{space 1}   -1.16{col 59}{space 3}0.246{col 67}{space 4}-1.827806{col 80}{space 3}  .468455
{txt}{space 17}student  {c |}{col 27}{res}{space 2} .2100444{col 39}{space 2} .1441428{col 50}{space 1}    1.46{col 59}{space 3}0.145{col 67}{space 4}-.0724702{col 80}{space 3}  .492559
{txt}{space 15}pensioner  {c |}{col 27}{res}{space 2} 2.416304{col 39}{space 2} .4024735{col 50}{space 1}    6.00{col 59}{space 3}0.000{col 67}{space 4}  1.62747{col 80}{space 3} 3.205137
{txt}{space 19}other  {c |}{col 27}{res}{space 2} .0693716{col 39}{space 2} .4716832{col 50}{space 1}    0.15{col 59}{space 3}0.883{col 67}{space 4}-.8551104{col 80}{space 3} .9938537
{txt}{space 25} {c |}
{space 19}income {c |}{col 27}{res}{space 2} .0624938{col 39}{space 2} .0650457{col 50}{space 1}    0.96{col 59}{space 3}0.337{col 67}{space 4}-.0649933{col 80}{space 3}  .189981
{txt}{space 25} {c |}
{space 17}religion {c |}
{space 20}Shia  {c |}{col 27}{res}{space 2} 6.546808{col 39}{space 2} .3747532{col 50}{space 1}   17.47{col 59}{space 3}0.000{col 67}{space 4} 5.812305{col 80}{space 3} 7.281311
{txt}{space 15}Christian  {c |}{col 27}{res}{space 2} .0245171{col 39}{space 2} .3546069{col 50}{space 1}    0.07{col 59}{space 3}0.945{col 67}{space 4}-.6704996{col 80}{space 3} .7195339
{txt}{space 25} {c |}
{space 16}ethnicity {c |}
{space 20}Kurd  {c |}{col 27}{res}{space 2} .8185329{col 39}{space 2} .3456581{col 50}{space 1}    2.37{col 59}{space 3}0.018{col 67}{space 4} .1410556{col 80}{space 3}  1.49601
{txt}{space 17}Turkmen  {c |}{col 27}{res}{space 2} 1.520015{col 39}{space 2} .8218158{col 50}{space 1}    1.85{col 59}{space 3}0.064{col 67}{space 4}-.0907139{col 80}{space 3} 3.130745
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}/cut1 {c |}{col 27}{res}{space 2}-.4148255{col 39}{space 2} .4397552{col 67}{space 4} -1.27673{col 80}{space 3} .4470788
{txt}{space 20}/cut2 {c |}{col 27}{res}{space 2} .8091645{col 39}{space 2} .4318207{col 67}{space 4}-.0371884{col 80}{space 3} 1.655517
{txt}{space 20}/cut3 {c |}{col 27}{res}{space 2} 1.646017{col 39}{space 2}  .431495{col 67}{space 4} .8003029{col 80}{space 3} 2.491732
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 4 observations completely determined.{txt}  Standard errors questionable.{p_end}

{com}. 
. *Sample Size and Power Analysis
. 
. *Effect Size Estimations using One-War ANOVA (Sample Size)
. power oneway, n(537) ngroups(3) power(0.80 0.90 0.95 0.99) graph
{res}{txt}
{com}. 
. *Effect Size Estimation using One-Way ANOVA (N per treatment group)
. power oneway, ngroups(3) n1(173) n2(175) n3(189) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:Ho: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N_avg}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:N3}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:537}{res}{ralign 8:179}{res}{ralign 8:173}{res}{ralign 8:175}{res}{ralign 8:189}{res}{ralign 8:.1343}{res}{ralign 8:3}{res}{ralign 8:.01804}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:537}{res}{ralign 8:179}{res}{ralign 8:173}{res}{ralign 8:175}{res}{ralign 8:189}{res}{ralign 8:.1539}{res}{ralign 8:3}{res}{ralign 8:.0237}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:537}{res}{ralign 8:179}{res}{ralign 8:173}{res}{ralign 8:175}{res}{ralign 8:189}{res}{ralign 8:.1701}{res}{ralign 8:3}{res}{ralign 8:.02892}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:537}{res}{ralign 8:179}{res}{ralign 8:173}{res}{ralign 8:175}{res}{ralign 8:189}{res}{ralign 8:.2002}{res}{ralign 8:3}{res}{ralign 8:.04007}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. *Cohen’s D for local police power grab txt relative to power-sharing
. cohend revpostalpha localpolicepgtxt if hashdpgtxt~=1

{res}(1) Cohen's {it:d} and (2) Cohen's {it:d} corrected for uneven groups

{txt}(1) -.93352934
(2) -.93354476


{res}(3) Hedges' {it:g} and (4) Hedges' {it:g} corrected for uneven groups

{txt}(3) -.93084292
(4) -.93085829


{res}(5) Effect size {it:r} and (6) Effect size {it:r} corrected for uneven groups

{txt}(5) -.42295839
(6) -.42295265

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\SS Powergrabbing Replication Files\SS_Powergrabbing_replication_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Nov 2023, 20:36:54
{txt}{.-}
{smcl}
{txt}{sf}{ul off}